Bayesian Classification

Bayesian network based classifiers are only able to handle discrete variables. They assume that variables are sampled from a multinomial distribution and most real-world domains involves continuous variables. A common practice to deal with continuous variables is to discretize them, with a subsequent loss of information. The continuous classifiers presented in this paper are supported by the Gaussian network paradigm, which assumes that variables follow a Gaussian distribution. A great advantage of Gaussian network is that they need O(n 2 ) parameters to model a complete graph. This work shows how classifiers, supported by the Bayesian network paradigm, can be adapted to deal with continuous variables without discretizing them. In addition, two novel classifier learning algorithms are introduced. The presented learning algorithms are ordered and grouped according to their structural complexity: from the simplest naive Bayes structures to k-dependence Bayesian classifiers and semi naive Bayes. Moreover, for each structure a filter and wrapper approaches are presented. All these classifiers are empirically evaluated using the Brier score and the predictive accuracy. The obtained results with both scores suggest that semi naive Bayes is the best classifier.

We present a system that is capable of segmenting, detecting and tracking multiple people in a cluttered scene using multiple synchronized cameras located far from each other. The system improves upon existing systems in many ways including: (1) We do not assume that a foreground connected component belongs to only one object; rather, we segment the views taking into account color models for the objects and the background. This helps us to not only separate foreground regions belonging to different objects, but to also obtain better background regions than traditional background subtraction methods (as it uses foreground color models in the algorithm). (2) It is fully automatic and does not require any manual input or initializations of any kind. (3) Instead of taking decisions about object detection and tracking from a single view or camera pair, we collect evidences from each pair and combine the evidence to obtain a decision in the end. This helps us to obtain much better detection and tracking as opposed to traditional systems. Several innovations help us tackle the problem. The first is the introduction of a region-based stereo algorithm that is capable of finding 3D points inside an object if we know the regions belonging to the object in two views. No exact point matching is required. This is especially useful in wide baseline camera systems where exact point matching is very difficult due to self-occlusion and a substantial change in viewpoint. The second contribution is the development of a scheme for setting priors for use in segmentation of a view using bayesian classification. The scheme, which assumes knowledge of approximate shape and location of objects, dynamically assigns priors for different objects at each pixel so that occlusion information is encoded in the priors. The third contribution is a scheme for combining evidences gathered from different camera pairs using occlusion analysis so as to obtain a globally optimum detection and tracking of objects. The system has been tested using different density of people in the scene which helps us to determine the number of cameras required for a particular density of people.

In this paper, the use of Independent Component Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a non-parametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by independent components. The method is based on the use of Independent Component Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. Then, a non parametric estimation of the density function is computed for each independent component. Finally, the Bayes rule is applied for classification assignment. The obtained results are compared with one of the most used classifier of hyperspectral images (Support Vector Machine) and show the comparative effectiveness of the proposed method.

In this paper, the use of Independent Component (IC) Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a nonparametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by ICs. The method uses IC Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. When the data are projected in an independent space, the estimates of their multivariate density function can be computed in a much easier way as the product of univariate densities. A nonparametric kernel density estimator is used to compute the density functions of each IC. Finally, the Bayes rule is applied for the classification assignment. In this paper, we investigate the possibility of using ICDA for the classification of hyperspectral images. We study the influence of the algorithm used to enforce independence and of the number of IC retained for the classification, proposing an effective method to estimate the most suitable number. The proposed method is applied to several hyperspectral images, in order to test different data set conditions (urban/agricultural area, size of the training set, and type of sensor). Obtained results are compared with one of the most commonly used classifier of hyperspectral images (support vector machines) and show the comparative effectiveness of the proposed method in terms of accuracy.

In this paper, the use of Independent Component Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a non-parametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by independent components. The method is based on the use of Independent Component Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. Then, a non parametric estimation of the density function is computed for each independent component. Finally, the Bayes rule is applied for classification assignment. The obtained results are compared with one of the most used classifier of hyperspectral images (Support Vector Machine) and show the comparative effectiveness of the proposed method.

This paper proposes the use of computed tomography (CT) as a reference method for estimating the lean meat percentage (LMP) of pig carcasses. The current reference is manual dissection which has a limited accuracy due to variability between butchers. A contextual Bayesian classification scheme is applied to classify volume elements of full body CT-scans of pig carcasses into three tissue types. A linear model describes the relation between voxels and the full weight of the half carcass, which can be determined more accurately than that of the lean meat content. Two hundred and ninety-nine half pig carcasses were weighed and CT-scanned. The explained variance of the model was R 2 ¼ 0:9994 with a root-meansquared error of prediction of 83.6 g. Applying this method as a reference will ensure a more robust calibration of sensors for measuring the LMP, which is less prone to variation induced by manual intervention.

We developed a classification approach to multiple quantitative trait loci (QTL) mapping built upon a Bayesian framework that incorporates the important prior information that most genotypic markers are not cotransmitted with a QTL or their QTL effects are negligible. The genetic effect of each marker is modeled using a three-component mixture prior with a class for markers having negligible effects and separate classes for markers having positive or negative effects on the trait. The posterior probability of a marker's classification provides a natural statistic for evaluating credibility of identified QTL. This approach performs well, especially with a large number of markers but a relatively small sample size. A heat map to visualize the results is proposed so as to allow investigators to be more or less conservative when identifying QTL. We validated the method using a well-characterized data set for barley heading values from the North American Barley Genome Mapping Project. Application of the method to a new data set revealed sex-specific QTL underlying differences in glucose-6-phosphate dehydrogenase enzyme activity between two Drosophila species. A simulation study demonstrated the power of this approach across levels of trait heritability and when marker data were sparse.

This paper introduces a evolutionary computation method that applies Bayesian classifiers to optimization problems. This approach is based on Estimation of Distribution Algorithms (EDAs) in which Bayesian or Gaussian networks are applied to the evolution of a population of individuals (i.e. potential solutions to the optimization problem) in order to improve the quality of the individuals of the next generation. Our new approach, called Evolutionary Bayesian Classifier-based Optimization Algorithm (EBCOA), employs Bayesian classifiers instead of Bayesian or Gaussian networks in order to evolve individuals to a fitter population. In brief, EBCOAs are characterized by applying Bayesian classification techniques -usually applied to supervised classification problems -to optimization in continuous domains. We propose and review in this paper different Bayesian classifiers for implementing our EBCOA method, focusing particularly on EBCOAs applying naïve Bayes, semi-naïve Bayes, and tree augmented naïve Bayes classifiers. This work presents a deep study on the behavior of these algorithms with classical optimiztion problems in continuous domains. The different parameters used for tuning the performance of the algorithms are discussed, and a comprehensive overview of their influence is provided. We also present experimental results to compare this new method with other state of the art approaches of the evolutionary computation field for continuous domains such as Evolutionary Strategies (ES) and Estimation of Distribution Algorithms (EDAs).

Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier-either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one-is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature.

We present a rapid method for the identification of viruses using microfluidic chip gel electrophoresis (CGE) of highcopy number proteins to generate unique protein profiles. Viral proteins are solubilized by heating at 95°C in borate buffer containing detergent (5 min), then labeled with fluorescamine dye (10 s), and analyzed using the µChem-Lab CGE system (5 min). Analyses of closely related T2 and T4 bacteriophage demonstrate sufficient assay sensitivity and peak resolution to distinguish the two phage. CGE analyses of four additional viruses-MS2 bacteriophage, Epstein-Barr, respiratory syncytial, and vaccinia viruses-demonstrate reproducible and visually distinct protein profiles. To evaluate the suitability of the method for unique identification of viruses, we employed a Bayesian classification approach. Using a subset of 126 replicate electropherograms of the six viruses and phage for training purposes, successful classification with nontraining data was 66/69 or 95% with no false positives. The classification method is based on a single attribute (elution time), although other attributes such as peak width, peak amplitude, or peak shape could be incorporated and may improve performance further. The encouraging results suggest a rapid and simple way to identify viruses without requiring specialty reagents such as PCR probes and antibodies.

Summary We analyse data from a study involving 173 pregnant women. The data are observed values of the β human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better th...

Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification?Location: The Netherlands.Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three‐dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for...

A wide panoply of machine learning methods is available for application to the Predictive Toxicology Evaluation (PTE) problem. The authors have built four monolithic classification systems based on Tilde, Progol, C4.5 and naive bayesian classification. These systems have been trained using the PTE dataset, and their accuracy has been tested using the unseen PTE1 data set as test set. A Multi Agent Decision System (MADES) has been built using the aforementioned monolithic systems to build classification agents. The MADES was trained and tested with the same data sets used with the monolithic systems. Results show that the accuracy of the MADES improves the accuracies obtained by the monolithic systems. We believe that in most real world domains the combination of several approaches is stronger than the individuals.

This paper proposes a supervised multiscale Bayesian texture classifier. The classifier exploits the dual-tree complex wavelet transform (DT-CWT) to obtain complex-valued multiscale representations of training texture samples for each texture class. The high-pass subbands of DT-CWT decomposition of a texture image are used to form a multiscale feature vector representing magnitude and phase features. For computational efficiency, the dimensionality of feature vectors is reduced using principal component analysis (PCA). The class conditional probability density function of low-dimensional feature vectors for each texture class is then estimated by using Parzen-window estimate with identical Gaussian kernels and is used to represent the texture class. A query texture image is classified as the corresponding texture class with the highest a posteriori probability according to a Bayesian inferencing. The superior performance and robustness of the proposed classifier is demonstrated for classifying texture images from image databases. The proposed multiscale texture feature vector extracted from both magnitude and phase of DT-CWT subbands of a query image is also shown to be effective for texture retrieval.

The medical automatic annotation task issued by the cross language evaluation forum (CLEF) aims at a fair comparison of state-of-the art algorithms for medical content-based image retrieval (CBIR). The contribution of this work is twofold: at first, a logical decomposition of the CBIR task is presented, and key elements to support the relevant steps are identified: (i) implementation of algorithms for feature extraction, feature comparison, and classifier combination, (ii) visualization of extracted features and retrieval results, (iii) generic evaluation of retrieval algorithms, and (iv) optimization of the parameters for the retrieval algorithms and their combination. Data structures and tools to address these key elements are integrated into an existing framework for image retrieval in medical applications (IRMA). Secondly, baseline results for the CLEF annotation tasks 2005-2007 are provided applying the IRMA framework, where global features and corresponding distance measures are combined within a nearest neighbor approach. Using identical classifier parameters and combination weights for each year shows that the task difficulty decreases over the years. The declining rank of the baseline submission also indicates the overall advances in CBIR concepts. Furthermore, a rough comparison between participants who submitted in only one of the years becomes possible.

Methods for predicting protein function from structure are becoming more important as the rate at which structures are solved increases more rapidly than experimental knowledge. As a result, protein structures now frequently lack functional annotations. The majority of methods for predicting protein function are reliant upon identifying a similar protein and transferring its annotations to the query protein. This method fails when a similar protein cannot be identified, or when any similar proteins identified also lack reliable annotations. Here, we describe a method that can assign function from structure without the use of algorithms reliant upon alignments. Using simple attributes that can be calculated from any crystal structure, such as secondary structure content, amino acid propensities, surface properties and ligands, we describe each enzyme in a non-redundant set. The set is split according to Enzyme Classification (EC) number. We combine the predictions of one-class versus one-class support vector machine models to make overall assignments of EC number to an accuracy of 35% with the top-ranked prediction, rising to 60% accuracy with the top two ranks. In doing so we demonstrate the utility of simple structural attributes in protein function prediction and shed light on the link between structure and function. We apply our methods to predict the function of every currently unclassified protein in the Protein Data Bank.

This paper addresses the use of multichannel signal processing methods in analysis of heart rate changes during cycling using the global positioning system (GPS) to record the route conditions. The main objectives of this work are in monitoring of physiological activities, cycling features extraction, their classification and visualization. Real data were acquired from 41 cycling rides of the same 11.48km long route divided into 2460 segments of approximately 60 s. The data were recorded with a varying sampling period within the range of 1-22 s depending on the route profile.

We present a rapid method for the identification of viruses using microfluidic chip gel electrophoresis (CGE) of highcopy number proteins to generate unique protein profiles. Viral proteins are solubilized by heating at 95°C in borate buffer containing detergent (5 min), then labeled with fluorescamine dye (10 s), and analyzed using the µChem-Lab CGE system (5 min). Analyses of closely related T2 and T4 bacteriophage demonstrate sufficient assay sensitivity and peak resolution to distinguish the two phage. CGE analyses of four additional viruses-MS2 bacteriophage, Epstein-Barr, respiratory syncytial, and vaccinia viruses-demonstrate reproducible and visually distinct protein profiles. To evaluate the suitability of the method for unique identification of viruses, we employed a Bayesian classification approach. Using a subset of 126 replicate electropherograms of the six viruses and phage for training purposes, successful classification with nontraining data was 66/69 or 95% with no false positives. The classification method is based on a single attribute (elution time), although other attributes such as peak width, peak amplitude, or peak shape could be incorporated and may improve performance further. The encouraging results suggest a rapid and simple way to identify viruses without requiring specialty reagents such as PCR probes and antibodies.

In this paper, a real time application for visual inspection and classification of cork stoppers is presented. Each cork stopper is represented by a high dimensional set of characteristics corresponding to relevant visual features. We have applied a set of nonparametric and parametric methods in order to compare and evaluate their performance for this real problem. The best results have been achieved using Bayesian classification through probabilistic modeling in a high dimensional space. In this context, it is well known that high dimensionality does not allow precision in the density estimation. We propose a Class-Conditional Independent Component Analysis (CC-ICA) representation of the data that even in low dimensions, performs comparably to standard classification techniques. The method has achieved a success of 98% of correct classification. Our prototype is able to inspect the cork stoppers and classify in 5 quality groups with a speed of 3 objects per second.

Summary Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frameworks, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards the use of complementary deoxyribonucleic acid microarrays for the classi-fication of tumours. These high throughput assays provide relative messenger ribonucleic acid expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. Gene expression profiles may offer more information than classical morphology and may provide an alternative to classical tumour diagnosis schemes. The paper considers several Bayesian classification...

This paper deals with new methods capable of solving the optimization problem concerning the allocation of DNA samples in plates in order to carry out the DNA sequencing with the Sanger technique. These methods make it possible to work with independent subproblems of lower complexity, obtaining solutions of good quality while maintaining a competitive time cost. They are compared with the ones introduced in the literature, obtaining interesting results. All the comparisons among the methods in the literature and the laboratory results have been made with real data.

The conventional seismic inversion approach is practical for operational work, as it only uses simple linearized algorithms and assumptions, but may be less applicable when dealing with a complex geological setting, especially in the Malay basin fields, as it may introduce non-linear noises and non-unique solutions. In the Malay basin, we also frequently struggle with a scarcity of reliable well data when performing seismic inversions. This makes finding an accurate prior model for inversion challenging and contributes to high uncertainty in properties’ estimation. Implementation of deep learning for seismic inversion has become routine and has shown increasing capability in addressing nonlinearity in inverse problems. In this work, we develop a robust approach to deep learning-based seismic inversion to predict elastic properties from seismic data. The approach incorporates synthetic well and seismic data generation from a set of rock physics knowledge called the rock physics libra...

In order to obtain potentially interesting patterns and relations from large, distributed, heterogeneous databases, it is essential to employ an intelligent and automated KDD (Knowledge Discovery in Databases) process. One of the most important methodologies is an integration of diverse learning strategies that cooperatively performs a variety of techniques and achieves high quality knowledge. AqBC is a multistrategy knowledge discovery approach that combines supervised inductive learning and unsupervised Bayesian classification. This study investigates creating a more suitable knowledge representation space with the aid of unsupervised Bayesian classification system, AutoClass. AutoClass discovers interesting patterns from databases. Via constructive induction, these patterns modify the knowledge representation space so that the robust inductive learning system, AQ15c, learns useful concept descriptions of a taxonomy. AqBC applied to two different sample problems yields not only simple but also meaningful knowledge due to the systems that implement its parent approaches. AqBC's good performance appears to be due to its integration of reliable unsupervised Bayesian classification, constructive induction and rule induction, and not to the presence of any component alone.

In order to obtain potentially interesting patterns and relations from large, distributed, heterogeneous databases, it is essential to employ an intelligent and automated KDD (Knowledge Discovery in Databases) process. One of the most important methodologies is an integration of diverse learning strategies that cooperatively performs a variety of techniques and achieves high quality knowledge. AqBC is a multistrategy knowledge discovery approach that combines supervised inductive learning and unsupervised Bayesian classification. This study investigates creating a more suitable knowledge representation space with the aid of unsupervised Bayesian classification system, AutoClass. AutoClass discovers interesting patterns from databases. Via constructive induction, these patterns modify the knowledge representation space so that the robust inductive learning system, AQ15c, learns useful concept descriptions of a taxonomy. AqBC applied to two different sample problems yields not only simple but also meaningful knowledge due to the systems that implement its parent approaches. AqBC's good performance appears to be due to its integration of reliable unsupervised Bayesian classification, constructive induction and rule induction, and not to the presence of any component alone.

Properly organizing knowledge so that it can be managed often requires the acquisition of patterns and relations from large, distributed, heterogeneous databases. The employment of an intelligent and automated KDD (Knowledge Discovery in Databases) process plays a major role in solving this problem. An integration of diverse learning strategies that cooperatively performs a variety of techniques and develops high quality knowledge can be a productive methodology for addressing this process. AqBC is a multistrategy knowledge discovery approach that combines supervised inductive learning and unsupervised Bayesian classification. This study investigates reorganizing the knowledge with the aid of an unsupervised Bayesian classification system, AutoClass. AutoClass discovers interesting taxonomies from databases. These taxonomies can also be represented as new attributes via constructive induction. The robust inductive learning system, AQ15c, can then learn useful concept relationships b...

Positional accuracy of geographic objects is treated in this contribution. Two main sources of uncertainty that influences the positional accuracy are considered: • uncertainty of classification, • uncertainty of georeferencing (image registration). These two sources are embraced in the overall spatial precision of geographic objects. It is assumed that several geographic objects are given as a result of Bayesian classification of a given digital (raster) image. It means that these objects are not determined precisely as crisp objects. Georeferencing of the given image is supposed to be determined by a linear transformation, coefficients of which were estimated by means of Bayesian approach. This transformation is therefore imprecise as well. It is designed here how to properly combine these two partial imprecise results to get the overall spatial precision of each geographic object in the given image. The resulting individual geographic object is expressed by its boundary which is ...

Risk approaches …………………………………………………………… EU Water Framework Directive (WFD) approach ……………….. International conservation status classification (IUCN) approach ... Key environmental correlates ……………………………………………... Physical variables …………………………………………………………. Bayesian MCMC population models ……………………………………… Species …………………………………………………………….. Methods …………………………………………………………… Priors ……………………………………………………………… Data ……………………………………………………………….. Posteriors …………………………………………………………. Population Bayesian networks in Hugin …………………………. Future development ………………………………………………. Prediction models for the Estonian WFD variables ……………………… Data availability and analyses ……………………………………. ZKI Index of macrozoobenthos community ……………………… Valuation ….….…………………………………………………………… Decision support tool-EVAGULF RADSS ……………………………... System architecture ………………………………………………... Graphical user interface …………………………………………… Results …………………………………………………………………….. Sensitivity of the Gulf of Finland food web to nutrient reductions ………. Background ……………………………………………………….. Materials and methods ……………………………………………. Results …………………………………………………………….. Discussion ………………………………………………………….

In this paper, we present the theoretical foundation for optimal classification using class-specific features and provide examples of its use. A new probability density function (PDF) projection theorem makes it possible to project probability density functions from a low-dimensional feature space back to the raw data space. An-ary classifier is constructed by estimating the PDFs of class-specific features, then transforming each PDF back to the raw data space where they can be fairly compared. Although statistical sufficiency is not a requirement, the classifier thus constructed will become equivalent to the optimal Bayes classifier if the features meet sufficiency requirements individually for each class. This classifier is completely modular and avoids the dimensionality curse associated with large complex problems. By recursive application of the projection theorem, it is possible to analyze complex signal processing chains. We apply the method to feature sets including linear functions of independent random variables, cepstrum, and MEL cepstrum. In addition, we demonstrate how it is possible to automate the feature and model selection process by direct comparison of log-likelihood values on the common raw data domain.

In this paper, we present a novel method for classification of cancer cell line images using complex waveletbased region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. CWT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method.

Eastern offshore basin (Fig. 1) is one of the most promising deep water block with several discoveries. After extensive exploration, it is now under development phase. Reservoirs in this area are sands within Godavari clay of Plio-Pleistocene age. These reservoirs are the slope channel sands which occur as high amplitude anomaly bursts within low to moderate amplitudes pertaining to encasing clay in the background. Capturing these high amplitude anomalies as geobodies bring out the geometry of NW-SE trending slope channel sands and the data from the drilled wells targeting them confirm that these high amplitude bursts to be correspond to coarser clastics. Most of these sand bodies are charged with hydrocarbons. However, as observed in some wells these high amplitude events, though correspond to reservoir sands, are not always hydrocarbon bearing. These surprises may pose a challenge during development phase. To understand and overcome this challenge, a reservoir characterization stu...

This paper describes AutoClass H, a program for automatically discovering (inducing) classes from a database, based on a Bayesian statistical technique which automatically determines the most probable number of classes, their probabilistic descriptions, and the probability that each object is a member of each class. AutoClass has been tested on several large, real databases and has discovered previously unsuspected classes. There is no doubt that these classes represent new phenomena.

We describe AutoClass, an approach to unsupervised classi cation based upon the classical mixture model, supplemented by a B a yesian method for determining the optimal classes. We include a moderately detailed exposition of the mathematics behind the AutoClass system. We emphasize that no current unsupervised classi cation system can produce maximally useful results when operated alone. It is the interaction between domain experts and the machine searching over the model space, that generates new knowledge. Both bring unique information and abilities to the database analysis task, and each enhances the others' e ectiveness. We illustrate this point with several applications of AutoClass to complex real world databases, and describe the resulting successes and failures.

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the Au-toClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. Simpler versions of AutoClass have been applied to many large real data sets, have discovered new independently-verified phenomena, and have been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters though a class hierarchy.

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Manet security has a lot of open issues. Due to its characteristics, this kind of network needs preventive and corrective protection. In this paper, we focus on corrective protection proposing an anomaly IDS model for Manet. The design and development of the IDS are considered in our 3 main stages: normal behavior construction, anomaly detection and model update. A parametrical mixture model is used for behavior modeling from reference data. The associated Bayesian classification leads to the detection algorithm. MIB variables are used to provide IDS needed information. Experiments of DoS and scanner attacks validating the model are presented as well.

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered. Index Terms-Bayesian classifier, prior probability, unmixing. I. INTRODUCTION I MAGE classification using conventional Bayesian classifier is a supervised method based on prior probabilities [1]. Prior probability resolves confusions among classes that are not well separable [2] and is therefore effective in improving Manuscript

Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification?Location: The Netherlands.Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three‐dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for...

Many approaches to image restoration are aimed at removing either Gaussian or uniform impulsive noise. This is because both types of degradation processes are distinct in nature, and hence they are easier to manage when considered separately. Nevertheless, it is possible to find them operating on the same image, which produces a hard damage. This happens when an image, already contaminated by Gaussian noise in the image acquisition procedure, undergoes impulsive corruption during its digital transmission. Here we propose a principled method to remove both types of noise. It is based on a Bayesian classification of the input pixels, which is combined with the kernel regression framework.

This study analyzes the PMB data in 2016. The PMB process at XYZ University has several paths, namely Regular, Transfer, and Bidikmisi. When the PMB process difficulties are encountered one of them is the number of prospective students who do not register again is increasing. Based on the identification of problems and information from officers the most important thing is the number of prospective students who do not re-register is increasing every year, thus affecting the impact of the number of PMB receipts. This study resolves these problems using the Bayesian Classification Algorithm so that the opportunity to re-register students can be known earlier. Based on the research conducted, it was concluded that the TDU class was 55% greater than the 45% DU class. This means that prospective students who do not completely re-register all study programs are larger. It was explained that PBSJ study programs 100% no prospective students who re-register. The Economic Education study progr...

Data Mining refers to using a variety of techniques to identify suggest of information or decision making knowledge in the database and extracting these in a way that they can put to use in areas such as decision support, predictions, forecasting and estimation. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Discovering relations that connect variables in a database is the subject of data mining. This research has developed a Decision Support in Heart Disease Prediction System (DSHDPS) using data mining modeling technique, namely, probability. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It is implemented as web based questionnaire application. It can serve a training tool to train nurses and medical students to diagnose patients with heart disease.

Feature representation is a very important factor that has great effect on the performance of speech recognition systems. In this paper we focus on a feature generation process that is based on linear transformation of the original log-spectral representation. We first discuss several three popular linear transformation methods, Mel-Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). We then prop ose a n ew m ethod of lin ear transformation that maximizes the normalized acoustic likelihood of the most likely state sequences of training data, a measure that directly related to our ultimate objective of reducing Bayesian classification error rate in speech recognition. Experimental results show that the proposed method decreases the relative word error rate by more than 9.1% compared to the best implementation of LDA, and by more than 25.9% compared to MFCC features.

An effective mask estimation scheme for missing-feature reconstruction is described that achieves robust speech recognition in the presence of unknown noise. In previous work on Bayesian classification for mask estimation, white noise and colored noise were used for training mask estimators. This paper, which is concerned with both the simulation of a more diverse set of background environments and with mitigating the "sparse training" problem, describes a new Bayesian mask-estimation procedure in which each frequency band is trained independently. The new method employs colored noise for training, which is obtained by partitioning each frequency subband. We also propose a reevaluation method of voiced/unvoiced decisions to alleviate performance degradation that is caused by errors in pitch detection. Experimental results indicate that the proposed procedure in conjunction with cluster-based missing-feature imputation improves speech recognition accuracy on the Aurora 2.0 database in the presence for all types of background noise considered.