CBR Systems

The complexity of current organization systems, and the increase in importance of the realization of internal controls in firms, make it necessary to construct models that automate and facilitate the work of auditors. An intelligent system has been developed to automate the internal control process. This system is composed of two case-based reasoning systems. The objective of the system is to facilitate the process of internal auditing in small and medium firms from the textile sector. The system, analyses the data that characterises each one of the activities carried out by the firm, then determines the state of each activity, calculates the associated risk, detects the erroneous processes, and generates recommendations to improve these processes. As such, the system is a useful tool for the internal auditor in order to make decisions based on the risk generated. Each one of the case-based reasoning systems that integrates the system uses a different problem solving method in each of the steps of the reasoning cycle: fuzzy clustering during the retrieval phase, a radial basis function network and a multi-criterion discreet method during the reuse phase and a rule based system for recommendation generation. The system has been proven successfully in several small and medium companies in the textile sector, located in the northwest of Spain. The accuracy of the technologies employed in the system has been demonstrated by the results obtained over the last two years. J.M. Corchado et al. The processes carried out inside a firm can be included in functional areas [19]. Each one of these areas is denominated a "Function". A Function is a group of coordinated and related activities, which are necessary to reach the objectives of the firm and are carried out in a systematic and reiterated way [11]. Functions are divided into activities, which are associated to well defined objectives. The functions that are usually carried out within a firm are: Purchases, Cash Management, Sales, Information Technology, Fixed Assets Management, Compliance to Legal Norms and Human Resources. In turn, each one of these functions is broken down into a series of activities. For example, the function Information Technology is divided in the following areas: Computer Plan Development, Study of Systems, Installation of Systems, Treatment of Information Flows and Security Management. Each activity is composed of a number of tasks, for example: register, authorise, approve, harmonise, separate obligations, operate, etc. Control procedures are established in the tasks to assure that the established objectives are achieved. Rulebased systems (RBS) have traditionally been used with the purpose of delimiting the audit decision-making tasks [6]. However, Messier and Hansen [13] found many situations in which auditors resolved problems by referring to previous situations. This contrasts with the very nature of RBS systems, since they have very little capacity for extracting information from past experience and present problems in order to adapt to changes in the environment. In contrast, case based reasoning systems (CBR) are able to relate past experiences or cases to current observations, solving new problems through the memorization and adaptation of previously tested solutions. This is an effective way of learning, similar to the general structure of human thought. CBR systems are especially suitable when the rules that define a knowledge system are difficult to obtain, or the number and complexity of the rules is too large to create an expert system. Moreover, CBR systems have the capacity to update their memory dynamically, based on new information (new cases), as well as, improving the resolution of problems [14]. However, in problems like those presented in this study, standard techniques of monitoring and prediction cannot be applied due to the complexity of the problem, the existence of certain preliminary knowledge, the great dynamism of the system, etc. In these types of systems it is necessary to use models that combine the advantages of several mechanisms of problem-solving capable to of resolving specific parts of the general problem and attending other parts. In this sense, an adaptive system has been developed. The system possesses the flexibility to behave in different ways and to evolve, depending on the environment in which it operates. The developed system is composed of two fundamental subsystems: Subsystem IEA (Identification of the State of the Activity) whose objectives are: 1. to identify the state or situation of each one of activities of the company. 2. to calculate the risk associated with this state. Subsystem GR (Generation of Recommendations), whose goal is: 1. generation of recommendations from the detection of inconsistent processes. These recommendations will allow the positive evolution of the internal processes of the company.

The AGM model [Alchourrón et al., 1985] has been criticized for not addressing the problem of iterated change. This is true, but in some cases the invalid claim that "AGM does not allow iteration" has been made. In this paper we examine the most elementary scheme of iteration: a binary operation * defined for any theory K and any formula α. We naturally extend the AGM model showing that it is in fact compatible with iteration. Following Alchourrón and Makinson's early reference to an iterated form of the safe contraction function, we provide extended constructions for each of the five AGM presentations (meet functions, systems of spheres, postulates, epistemic entrenchments and safe hierarchies) and prove their equivalence.

The prediction of the occurrence of short-term adverse events in phototherapy treatments is important for dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique that can detect the potential occurrence of short-term adverse events in phototherapy treatments is required for clinicians. Based on data mining, this study tends to explore the significant features and the class distribution of training data for predicting the occurrence of short-term adverse events in NB-UVB phototherapy treatments. The experimental results highlight that an acceptable prediction accuracy can be achieved using the significant features and the performance of the classifiers can be significantly improved by sampling 40% of the negative class samples in the training data, hyper-parameter tuning of the classifiers and use of stacked classifiers in creating the prediction models.

Recently the number of undesirable messages coming to e-mail has strongly increased. As spam has changeable character the anti-spam systems should be trainable and dynamical. The machine learning technology is successfully applied in a filtration of e-mail from undesirable messages for a long time. In this paper it is offered to apply Case Based Reasoning technology to a spam filtering problem. The possibility of continuous updating of spam templates base on the bases of which new coming spam messages are compared, will raise efficiency of a filtration. Changing a combination of conditions it is possible to construct flexible filtration system adapted for different users or corporations. Also in this paper it is considered the second approach as implementation of CRM technology to spam filtration which is not applied to this area yet.

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 3...

In the absence of fast and adequate measures to combat them, life-threatening diseases are catastrophic to human health. Computational intelligent algorithms characterized by their adaptability, robustness, diversity, and recognition abilities allow for the diagnosis of medical diseases. This enhances the decision-making process of physicians. The objective is to predict and classify diseases accurately. In this paper, we proposed a multi-agent stacked ensemble classifier based on a vaguely quantified rough set, simple logistic algorithm, sequential minimal optimization (SMO), and JRip. The vaguely quantified rough set (VQRS) is used for feature selection and eradicating noise in the data. There are two classifier layers in the stacked ensemble, with the simple logistic and SMO being the base classifiers, and JRip being the meta-classifier. The base classifiers learn using training data, and the individual classifier predictions are merged for input into the meta-classifier, which makes predictions accordingly. In experiments, the proposed method was validated by providing improved diagnoses with respect to three classification operations. The proposed method thus provides more accurate medical diagnoses to the compared algorithms.

Microarray technology allows to measure the expression levels of thousands of genes in an experiment. The use of computational methods is fundamental in cancer research. One of the possibilities is the use of Artificial Intelligence techniques. Several of these techniques have been used to analyze expression arrays. This paper presents a Case-based reasoning (CBR) system for automatic classification of leukemia patients from microarray Data. The system incorporates novel algorithms for data mining that allow to filter and classify as well as extraction of knowledge. The system has been tested and the results obtained are presented in this paper.

This paper presents a novel Ambient Intelligence based solution for shopping assistance. The core of the proposal is a CBR system developed for guiding and advising users in shopping areas. The CBR incorporates a neural based planner that identifies the most adequate plan for a given user based on user profile and interests. The RTPW neural network is based on the Kohonen one, and incorporates an interesting modification that allows a solution or a plan to be reached much more rapidly. Furthermore, once an initial plan has been reached, it is possible to identify alternatives by taking restrictions into account. The CBR system has been embedded within a deliberative agent and interacts with interface and commercial agents, which facilitate the construction of intelligent environments. This hybrid application, which works on execution time, has been tested and the results of the investigation and its evaluation in a shopping mall are presented within this paper.

In recent years, machine learning and data mining fields have found a successful application area in the field of DNA microarray technology. Gene expression profiles are composed of thousands of genes at the same time, representing complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of identifying an accurate gene selection strategy is crucial to reduce the generalization error (false positives) of state-of-the-art classification algorithms. This paper presents a reduction algorithm based on the notion of fuzzy gene expression, where similar (co-expressed) genes belonging to different patients are selected in order to construct a supervised prototype-based retrieval model. This technique is employed to implement the retrieval step in our new gene-CBR system. The proposed method is illustrated with the analysis of microarray data belonging to bone marrow cases from 43 adult patients with cancer plus a group of three cases corresponding to healthy persons.

The prediction of the occurrence of short-term adverse events in phototherapy treatments is important for dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique that can detect the potential occurrence of short-term adverse events in phototherapy treatments is required for clinicians. Based on data mining, this study tends to explore the significant features and the class distribution of training data for predicting the occurrence of short-term adverse events in NB-UVB phototherapy treatments. The experimental results highlight that an acceptable prediction accuracy can be achieved using the significant features and the performance of the classifiers can be significantly improved by sampling 40% of the negative class samples in the training data, hyper-parameter tuning of the classifiers and use of stacked classifiers in creating the prediction models.

This paper presents a novel Ambient Intelligence based solution for shopping assistance. The core of the proposal is a CBR system developed for guiding and advising users in shopping areas. The CBR incorporates a neural based planner that identifies the most adequate plan for a given user based on user profile and interests. The RTPW neural network is based on the Kohonen one, and incorporates an interesting modification that allows a solution or a plan to be reached much more rapidly. Furthermore, once an initial plan has been reached, it is possible to identify alternatives by taking restrictions into account. The CBR system has been embedded within a deliberative agent and interacts with interface and commercial agents, which facilitate the construction of intelligent environments. This hybrid application, which works on execution time, has been tested and the results of the investigation and its evaluation in a shopping mall are presented within this paper.

The paper is about processing of biomedical data. It were used 13 methods of machine learning (Naive Bayes classifier for multivariate Bernoulli models, A decision tree classifier, An extremely randomized tree classifier, Classifier implementing the k nearest neighbors vote, Linear Discriminant Analysis, Linear Support Vector Classification, Logistic Regression, Nearest centroid classifier, A random forest classifier, Classifier using Ridge regression, Ridge classifier with built in cross validation, Gaussian Mixture Models, Support Vector Machines) and one method of deep learning (Multiplayer Perception). A discrete wavelet transform was used to extract of biometric features. Haar wavelets, Daubechi wavelets, Symlets, Coiflets, Biorthogonal, Reverse biorthogonal, Discrete Meyer (FIR Approximation) were used. The influence of Electrocardiorams (ECG) recording time on the accuracy of biometric identification and diagnosis of cardiovascular diseases was studied. It was found that the best methods of classification are: Multiplayer Perception, An extremely randomized tree classifier, Classifier implementing the k nearest neighbors vote and Logistic Regression aka logit MaxEnt classifier. Wavelet family doesn't affect significantly on accuracy of recognition. With increasing registration time, accuracy increases .

In this paper we combine three recent technologies to provide e-Assistance. On the one hand Ambient Intelligence is an useful paradigm to help people dealing with unknown situations. On the other hand, Case Based Reasoning (CBR) applies knowledge gained through past experiences to solve current problems. Finally, peer-to-peer (P2P) networks let users to share resources among members of a community. A combination of these technologies can be used to build a seamless e-assistance system in order to help people using mobile ad-hoc networks and enabling nodes to establish spontaneous connections among them in order to meet specific needs. Taking this context as a starting point we have developed a CBR multi-agent system (MAS) that exchanges its cases through a P2P mobile ad-hoc network in order to solve daily problems. An example describing an intelligent gym for elder physical training is also provided.

We analyzed intestinal contents of two late-glacial mastodons preserved in lake sediments in Ohio (Burning Tree mastodon) and Michigan (Heisler mastodon). A multi-proxy suite of macrofossils and microfossils provided unique insights into what these individuals had eaten just before they died and added significantly to knowledge of mastodon diets. We reconstructed the mastodons’ habitats with similar multi-proxy analyses of the embedding lake sediments. Non-pollen palynomorphs, especially spores of coprophilous fungi differentiated intestinal and environmental samples. The Burning Tree mastodon gut sample originates from the small intestine. The Heisler mastodon sample is part of the large intestine to which humans had added clastic material to anchor parts of the carcass under water to cache the meat. Both carcasses had been dismembered, suggesting that the mastodons had been hunted or scavenged, in line with other contemporaneous mastodon finds and the timing of early human incursi...

The present study explored dichotomic classification methods for medical diagnosis data through three experiments. A first experiment run in Weka used four different classification schemes on two different sets of medical test data thus permitting comparison of each scheme’s performance. A second experiment tested the application of attribute selection, information gain, and boosting to Weka’s support vector classification scheme (SMO). Finally, in the third experiment when a cost matrix was applied to breast cancer diagnostic data, false negatives were effectively reduced to under one percent while overall accuracy was slightly improved. The first experiment suggests that SMO may classify better than J48, IBk and Naïve Bayes with respect to medical test data from the UCI repository. The data of the first experiment also suggests that support vector classification-based diagnosis outperforms manual diagnosis of fine needle aspirate results. While the second experiment showed no enha...

In this paper we combine three recent technologies to provide e-Assistance. On the one hand Ambient Intelligence is an useful paradigm to help people dealing with unknown situations. On the other hand, Case Based Reasoning (CBR) applies knowledge gained through past experiences to solve current problems. Finally, peer-to-peer (P2P) networks let users to share resources among members of a community.

Present study proposes the application of different soft-computing and statistical techniques to the characterization of atmospheric conditions in Spain. The main goal is to visualize and analyze the air quality in a certain region of Spain (Madrid) to better understand its circumstances and evolution. To do so, real-life data from three data acquisition stations are analysed. The main pollutants acquired by these stations are studied in order to research how the geographical location of these stations and the different seasons of the year are decisive in the behavior of air pollution. Different techniques for dimensionality reduction together with clustering techniques have been applied, in a combination of neural and fuzzy paradigms.

Present study proposes the application of different soft-computing and statistical techniques to the characterization of atmospheric conditions in Spain. The main goal is to visualize and analyze the air quality in a certain region of Spain (Madrid) to better understand its circumstances and evolution. To do so, real-life data from three data acquisition stations are analysed. The main pollutants acquired by these stations are studied in order to research how the geographical location of these stations and the different seasons of the year are decisive in the behavior of air pollution. Different techniques for dimensionality reduction together with clustering techniques have been applied, in a combination of neural and fuzzy paradigms.

Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed vis...

In recent years, machine learning and data mining fields have found a successful application area in the field of DNA microarray technology. Gene expression profiles are composed of thousands of genes at the same time, representing complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of identifying an accurate gene selection strategy is crucial to reduce the generalization error (false positives) of state-of-the-art classification algorithms. This paper presents a reduction algorithm based on the notion of fuzzy gene expression, where similar (co-expressed) genes belonging to different patients are selected in order to construct a supervised prototype-based retrieval model. This technique is employed to implement the retrieval step in our new gene-CBR system. The proposed method is illustrated with the analysis of microarray data belonging to bone marrow cases from 43 adult patients with cancer plus a group of three cases corresponding to healthy persons.

P. Perner (Ed.): ICDM 2004, LNAI 3275, pp. 42–49, 2004. © Springer-Verlag Berlin Heidelberg 2004 ... A Beta-Cooperative CBR System for Constructing a ... Emilio S. Corchado1, Juan M. Corchado2, Lourdes Sáiz1, and Ana Lara1 ... 1Department of Civil Engineering, ...

Background/Aim. Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods. Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results. The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion. Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of í µí±› = 25 as a cutoff point for RT bagging to outperform a single RT.

Data stream classification plays a vital role in data mining techniques which extracts the most important patterns from the real world database. Nowadays, many applications like sensor network, video surveillance and network traffic generate a huge amount of data streams. Due to the ambiguity in input data, imprecise input information and concept drift, some problems arise in classifying the data stream. To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function. At first, the input database is given to the PCA algorithm to reduce the dimension of the data. An adaptive neuro fuzzy classifier is utilized where the designing of membership function and rule base are the two important aspects. Then, neuro-fuzzy system undergoes updating when the change of detection occurs between the data streams. Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function. The experimental results are evaluated for the datasets and the performance is analysed by some metrics and compared with the existing systems such as JIT adaptive K-NN and HRFuzzy system. From the result, it is concluded that our proposed fuzzy classifier attains the higher accuracy of 96% which proves the efficient performance of data stream classification algorithm.

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising.

Knowledge management is a key element in medical environments. Arrays CGH make possible the realization of tests on patients for the detection of mutations in chromosomal regions. The detection of the regions with mutations associated to different pathologies is an important step for the selection of relevant genes, proteins or diseases. The corresponding information of the mutations and genes is distributed in different public sources and databases, so it is necessary the use of systems that allow to contrast different sources for the selection of outstanding information. In this work, a case-based reasoning (CBR) system is presented to carry out the automatic selection of relevant segments and the associated genes or proteins that could determine different pathologies. The CBR system integrates statistical techniques for the selection of relevant genes and visualization techniques for the interpretation of the final results.

The mission of the conference is to bring together researchers and people from industry in order to discuss together new trends and applications in data mining. This year a broad spectrum of work of different applications was presented ranging from image mining, medicine and biotechnology, management and environmental control, to telecommunications. Besides that an industrial exhibition showed the successful application of data mining methods by industries in different areas such as medical devices, mass data management systems, data mining tools, etc.

Microarray technology allows to measure the expression levels of thousands of genes in an experiment. The use of computational methods is fundamental in cancer research. One of the possibilities is the use of Artificial Intelligence techniques. Several of these techniques have been used to analyze expression arrays. This paper presents a Case-based reasoning (CBR) system for automatic classification of leukemia patients from microarray Data. The system incorporates novel algorithms for data mining that allow to filter and classify as well as extraction of knowledge. The system has been tested and the results obtained are presented in this paper.

In recent years, machine learning and data mining fields have found a successful application area in the field of DNA microarray technology. Gene expression profiles are composed of thousands of genes at the same time, representing complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of identifying an accurate gene selection strategy is crucial to reduce the generalization error (false positives) of state-of-the-art classification algorithms. This paper presents a reduction algorithm based on the notion of fuzzy gene expression, where similar (co-expressed) genes belonging to different patients are selected in order to construct a supervised prototype-based retrieval model. This technique is employed to implement the retrieval step in our new gene-CBR system. The proposed method is illustrated with the analysis of microarray data belonging to bone marrow cases from 43 adult patients with cancer plus a group of three cases corresponding to healthy persons.

Knowledge has become the most strategic resource in the new business environment. A case-based reasoning system has been developed for identifying critical situations in business processes. The CBR system can be used to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This technique is used as a tool to develop a part of a Global and Integral Model of Business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the data mining model tries to generalize the hypothesis of organizational survival and competitiveness, so that the organization that is able to identify, strengthen, and use key knowledge will reach a pole position. This case-based reasoning system incorporates a novel artificial neural architecture called Beta-Cooperative Learning in order to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This architecture is used to retrieve the most similar cases to a given subject.

CBR systems are normally used to assist experts in the resolution of problems. During the last few years researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a Maximum Likelihood Hebbian Learning-based method that automates the organisation of cases and the retrieval stage of case-based reasoning systems. The proposed methodology has been derived as an extension of the Principal Component Analysis, and groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.

This paper presents a method for automating the revise phase of case-based reasoning systems. The revision is carried out using a rule-based system, which adapts its rules or knowledge base as the working environment, to which the system is applied, evolves in time. This actualisation is carried out by a belief revision technique. An example has been introduced to illustrate the working mode of the revision technique.

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright Abstract The problem of unsolicited e-mail has been increasing during recent years. Fortunately, some advanced technologies have been successfully applied to spam filtering, achieving promising results. Recently, we have introduced SPAMHUNTING, a successful spam filter able to address the concept drift problem by combining a relevant term identification technique with an evolving sliding window strategy. Several successful spam filtering techniques use continuous learning strategies to achieve better adaptation capabilities and address concept drift issues. Nevertheless, due to the presence of concept drift and hidden changes in the environment, the presence of obsolete and irrelevant knowledge becomes a serious drawback. Soon after the launch of the filter, many decisions are made based on irrelevant and/or obsolete knowledge. Therefore, in such a situation, the use of forgetting strategies is as important as the implementation of continuous learning approaches. In this paper we introduce a novel technique designed for identifying and removing the obsolete and irrelevant knowledge that has accumulated over to the passage of time. We have carried out several experiments to test for the suitability of our proposal showing the results obtained and its applicability.

This paper presents a novel Ambient Intelligence based solution for shopping assistance. The core of the proposal is a CBR system developed for guiding and advising users in shopping areas. The CBR incorporates a neural based planner that identifies the most adequate plan for a given user based on user profile and interests. The RTPW neural network is based on the Kohonen one, and incorporates an interesting modification that allows a solution or a plan to be reached much more rapidly. Furthermore, once an initial plan has been reached, it is possible to identify alternatives by taking restrictions into account. The CBR system has been embedded within a deliberative agent and interacts with interface and commercial agents, which facilitate the construction of intelligent environments. This hybrid application, which works on execution time, has been tested and the results of the investigation and its evaluation in a shopping mall are presented within this paper.

Case-based Reasoning (CBR) is the process of solving a problem based on knowledge gained from past cases with similar solutions. Adaptive Case-Based Reasoning (ACBR) is an advanced form of instance based learning that is suitable to more complex objects. CBR is an interesting field to explore in the realm of Artificial Intelligence because it mimics human behaviour of problem solving. It can be, and are, widely used in expert systems. In this article, we implemented both CBR and ACBR and compared them using statistical method.

Abstract Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work well. Case-Based Reasoning (CBR) is a lazy approach to machine learning where induction is delayed to run time. This means that the case base can be updated continuously and new training data is immediately available to the induction process.

Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work well. Case-Based Reasoning (CBR) is a lazy approach to machine learning where induction is delayed to run time. This means that the case base can be updated continuously and new training data is immediately available to the induction process. In this paper we present a detailed description of such a system called ECUE and evaluate design decisions concerning the case representation. We compare its performance with an alternative system that uses Naïve Bayes (NB). We find that there is little to choose between the two alternatives in cross-validation tests on data sets. However, ECUE does appear to have some advantages in tracking concept drift over time.