Rough Sets

This paper is concerned with solving large scale linear fractional multiple objective programming (LSLFMOP) problems with chance constraints. Channce constraints involve random parameters in the right-hand sides. These random right-hand sides are considered to be statistically independent random variable. The main features of the proposed solution procedure are based on chanceconstrained technique, Charens and Cooper transformation and the Rosen's partitioning procedure. An illustrative numerical example is given to clarify the main results developed in the paper.

In this paper an attempt has been made to review the research studies on application of data mining techniques in the field of agriculture. Some of the techniques, such asID3 algorithms, the k-means, the k nearest neighbour, artificial neural networks and support vector machines applied in the field of agriculture were presented. Data mining in application in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. This article explores the applications of data mining techniques in the field of agriculture and allied sciences.

Types are a crucial concept in conceptual modelling, logic, and knowledge representation as they are an ubiquitous device to understand and formalise the classification of objects. We propose a logical treatment of types based on a cognitively inspired modelling that accounts for the amount of information that is actually available to a certain agent in the task of classification. We develop a predicative modal logic whose semantics is based on conceptual spaces that model the actual information that a cognitive agent has about objects, types, and the classification of an object under a certain type. In particular, we account for possible failures in the classification, for the lack of sufficient information, and for some aspects related to vagueness.

The focus  of this paper is on classification of different vehicles using sound emanated from the vehicles. In this paper,quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of periodic segments of signals. Simulation results show that just by considering high energy feature vectors, better classification accuracy can be achieved due to the correspondence of low energy regions with noises of the background. To separate these elements, short time energy and average zero cross rate are used simultaneously.In our method,we have used a few features which are easy to be calculated in time domain and enable practical implementation of efficient classifier. Although, the computation complexity is low, the classification accuracy is comparable with other classification methods based on long feature vectors reported in literature for this problem.

Data mining is an interdisciplinary research area spanning severals disciplines such as database systems, machine learning, intelligent information systems, statistics, and expert systems. Data mining has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Many aspects of data mining have been investigated in several related elds. A unique but important aspect of the problem lies in the signi cance of needs to extend these studies to include the nature of the contents of the real-world databases. In this chapter, we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. Since a major focus of this book is on rough sets and its applications to database mining, one full section is devoted to summarize the state of rough sets as related to data mining of real-world databases. More importantly we believe that theory of rough sets constitutes a sound basis for data mining applications.

An alternative numerical method for solving contact problems for real rough surfaces is proposed. The real area of contact and the contact pressure distribution are determined using a single-loop iteration scheme based on the conjugate gradient method, which converges for arbitrary rough surfaces. The surface deflections and subsurface stresses are computed using an alternative two-dimensional multi-level multi-summation algorithm, which allows the summation error to be kept under the discretization error for any number of contact points. The proposed method is fast: rough contact problems for surface samples with 10 5 -10 6 data points are solved on a personal computer in a few hours. The numerical algorithms are described in full detail so that an interested reader can implement the new contact solver in a computer code. Numerical examples demonstrating the method advantages are presented. The method is compared with other fast contact solvers that have emerged in the last few years. q 1999 Elsevier Science S.A. All rights reserved.

In this chapter we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. A section is devoted to summarizing the state of rough sets as related to data mining of real-world databases.

We argue that a cognitive semantics has to take into account the possibly partial information that a cognitive agent has of the world. After discussing Gärdenfors's view of objects in conceptual spaces, we offer a number of viable treatments of partiality of information and we formalize them by means of alternative predicative logics. Our analysis shows that understanding the nature of simple predicative sentences is crucial for a cognitive semantics.

Recently machine learning-based Intrusion Detection systems (IDs) have been subjected to extensive researches because they can detect both misuse and anomaly. Most of existing IDs use all features in the network packet to look for known intrusive patterns. Some of these features are irrelevant or redundant. Rough Set Classification (RSC), a modern learning algorithm, is used to rank features extracted for detecting intrusions and generate intrusion detection models.

In this paper a new hybrid model RSC-PGA (Rough Set Classification Parallel Genetic Algorithm) is presented to address the problem of identifying important features in building an intrusion detection system, increase the convergence speed and decrease the training time of RSC. Tests are done on KDD-99 dataset used for The Third International Knowledge Discovery and Data Mining Tools Competition. Results showed that the proposed model gives better and robust representation of rules as it was able to select features resulting in great data reduction, time reduction and error reduction in detecting new attacks.

This paper presents a new indiscernibility-based rough agglomerative hierarchical clustering algorithm for sequential data. In this approach, the indiscernibility relation has been extended to a tolerance relation with the transitivity property being relaxed. Initial clusters are formed using a similarity upper approximation. Subsequent clusters are formed using the concept of constrained-similarity upper approximation wherein a condition of relative similarity is used as a merging criterion. We report results of experimentation on msnbc web navigation dataset that are intrinsically sequential in nature. We have compared the results of the proposed approach with that of the traditional hierarchical clustering algorithm using vector coding of sequences. The results establish the viability of the proposed approach. The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group. Such descriptions can help web miners to identify potential and meaningful groups of users.

The non–equational notion of abstract approximation space for roughness theory is introduced, and its relationship with the equational definition of lattice with Tarski interior and closure operations is studied. Their categorical isomorphism is proved, and the role of the Tarski interior and closure with an algebraic semantic of a S4–like model of modal logic is widely investigated. A hierarchy of three particular models of this approach to roughness based on a concrete universe is described, listed from the stronger model to the weaker one: (1) the partition spaces, (2) the topological spaces by open basis, and (3) the covering spaces.

To date the availability of spatial data is increasing together with techniques and methods adopted in geographical analysis. Despite this tendency, classifying in a sharp way every part of the city is more and more com-plicated. This is due to the growth of city complexity. Rough Set theory may be a useful method to employ in combining great amounts of data in order to build complex knowledge about territory. It represents a different mathemati-cal approach to uncertainty by capturing the indiscernibility. Two different phenomena can be indiscernible in some contexts and classified in the same way when combining available information about them. Several experiences exist in the use of Rough Set theory in data mining, knowledge analysis and approximate pattern classification, but the spatial component lacks in all these research streams.
This paper aims to the use of Rough Set methods in geographical analyses. This approach has been applied in a case of study, comparing the results achieved by means of both Map Algebra technique and Spatial Rough set. The study case area, Potenza Province, is particularly suitable for the application of this theory, because it includes 100 municipalities with a different number of inhabitants and morphologic features.

Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough setbased research directions and applications.

We present some methods, based on the rough set and Boolean reasoning approaches, for extracting laws from decision tables. First we discuss several procedures for decision rules synthesis from decision tables. Next we show how to apply some near-to-functional relations between data to data filtration. Two methods of searching for new classifiers (features) are described: searching for new classifiers in a given set of logical formulas, and searching for some functions approximating near-to-functional relations.

Prediction plays a significant role in the human life to predict the situation, climate, finance, outcome of the particular event or activities, etc. This predication can be achieved by the classifier which is formally known as supervised learner. The classifier can be built using the dataset and its performance is based on the attributes or features present in the dataset which are highly relevant to the predictive target attributes. The feature selection process removes the redundant and irrelevant features from the dataset to improve the performance of the classifier. This paper proposes a rough set-based feature selection method to remove the redundant and irrelevant features in order to improve the performance the classifier. The proposed method is tested on the various datasets with the various supervised learning algorithms and it is evident that the proposed method producing the better performance than the other methods compared.

In this paper, we generalized the notions of rough set concepts using two topological structures generated by general binary relation defined on the universe of discourse. New types of topological rough sets are initiated and studied using new types of topological sets. Some properties of topological rough approximations are studied by many propositions.

A new feature selection method based on kernelized fuzzy rough sets (KFRS) and the memetic algorithm (MA) is proposed for transient stability assessment of power systems. Considering the possible real-time information provided by wide-area measurement systems, a group of system-level classification features are extracted from the power system operation parameters to build the original feature set. By defining a KFRS-based generalized classification function as the separability criterion, the memetic algorithm based on binary differential evolution (BDE) and Tabu search (TS) is employed to obtain the optimal feature subsets with the maximized classification capability. The proposed method may avoid the information loss caused by the feature discretization process of the rough-set based attribute selection, and comprehensively utilize the advantages of BDE and TS to improve the solution quality and search efficiency. The effectiveness of the proposed method is validated by the application results on the New England 39-bus power system and the southern power system of Hebei province.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of  soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field

Gene selection is a main procedure of discriminate analysis of microarray data which is the process of selecting most informative genes from the whole gene data base. This paper approach a method for selecting informative genes by using Rough Set Theory. Rough Set Theory is a effective mathematical tool for selecting informative genes. This paper describes basics of Rough Set Theory and Rough Set attribute reduction by Quick -Reduct based Genetic Algorithm.

We propose a new feature selection strategy based on rough sets and Particle Swarm Optimization (PSO). Rough sets has been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. On the other hand, complete searches are not feasible for even medium-sized datasets. So, stochastic approaches provide a promising feature selection mechanism. Like Genetic Algorithms, PSO is a new evolutionary computation technique, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The Particle Swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for feature selection in that particle swarms will discover best feature combinations as they fly within the subset space. Compared with GAs, PSO does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that PSO is efficient for rough set-based feature selection.

Quality of Experience (QoE) is emerging as the holy grail of human-centric multimedia services. QoE is a multi-disciplinary field based on social psychology, cognitive science, economics and engineering science, focused on understanding overall human quality of experience requirements. QoE has been viewed as the pivotal set of metrics in determining the success or failure of any product or service. In real time environment, QoE is influenced by multiple service factors such as application and network level QoS parameters, content, and business aspects. We emulate wireless environment and analyze the combined impact of the network and application level QoS parameters and content characteristics over user perceived quality for video streaming service. We use Rough Set Theory (RST) for quantitative assessment and simple CCA frame work for qualitative assessment of user data in order to understand the influence of multiple multimedia service parameters over QoE.

This paper deals with knowledge acquisition in incomplete information systems using rough set theory. The concept of similarity classes in incomplete information systems is first proposed. Two kinds of partitions, lower and upper approximations, are then formed for the mining of certain and association rules in incomplete decision tables. One type of ''optimal certain'' and two types of ''optimal association'' decision rules are generated. Two new quantitative measures, ''random certainty factor'' and ''random coverage factor'', associated with each decision rule are further proposed to explain relationships between the condition and decision parts of a rule in incomplete decision tables. The reduction of descriptors and induction of optimal rules in such tables are also examined.

The paper presents a transition from the crisp rough set theory to a fuzzy one, called Alpha Rough Set Theory or, in short, a-RST. All basic concepts or rough set theory are extended, i.e., information system, indiscernibility, dependency, reduction, core, de®nability, approximations and boundary. The resulted theory takes into account fuzzy data and allows the approximation of fuzzy concepts. Besides, the control of knowledge granularity is natural in a-RST which is based on a parameterized indiscernibility relation. a-RST is developed to recognize non-deterministic relationships using notions as a-dependency, a-reduct and so forth. On the other hand, we introduce a notion of relative dependency as an alternative of the absolute de®nibility presented in rough set theory. The extension a-RST leads naturally to the new concept of alpha rough sets which represents sets with fuzzy non-empty boundaries. Ó

Rough set theory (RST) offers an interesting and novel approach both to the generation of rules for use in expert systems and to the traditional statistical task of classification. The method is based on a novel classification metric, implemented as upper and lower approximations of a set and more generally in terms of positive, negative and boundary regions. Classification accuracy, which may be set by the decision maker, is measured in terms of conditional probabilities for equivalence classes, and the method involves a search for subsets of attributes (called 'reducts') which do not require a loss of classification quality. To illustrate the technique, RST is employed within a state level comparison of education expenditure in the USA.

Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. In this paper , irrelevant genes are eliminated in two stages, employing correlation-based feature selection (CFS)as an evaluator and binary particle swarm optimization (BPSO)as a search technique at the first phase and in the second phase of elimination it (CFS+PSO) combines Quick-Reduct algorithm and forms an integrated filter method. Since the data consist of a large number of redundant features, an initial redundancy reduction of the gene is done to enable faster convergence. Then Rough set theory is employed to generate reducts, which represent the minimal sets of non-redundant gene capable of discerning between all objects, in a multi-objective framework. The effectiveness of the proposed approach was verified on two different multi-class microarray datasets using K-nearest neighbour (K-NN), Naïve Bayes( NB) and J48 classifiers with leave-one-out cross-validation (LOOCV) method.

The present study aims to identify the relationship between individuals' multiple intelligence areas and their learning styles with mathematical clarity using the concept of rough sets which is used in areas such as artificial intelligence, data reduction, discovery of dependencies, prediction of data significance, and generating decision (control) algorithms based on data sets. Therefore, first multiple intelligence areas and learning styles of 243 mathematics prospective teachers studying at a state university were identified using the "Multiple Intelligence Inventory for Educators" developed by Armstrong and the "Learning Styles Scale" developed by Kolb. Second, the data was appropriated for rough set analysis and we identified potential learning styles that a student can have based on the learning style s/he already has. Certainty degrees of the learning style sets were α R (D) ≅ 0.717, α R (C) ≅ 0.618, α R (AS) ≅ 0.699, α R (AC) ≅ 0.461, and these sets were found to be rough sets. Finally, decision rules were identified for multiple intelligences and learning styles.

Using as example an incomplete information system with support a set of objects X, we discuss a possible algebraization of the concrete algebra of the power set of X through quasi BZ lattices. This structure enables us to define two rough approximations based on a similarity and on a preclusive relation, with the second one always better that the former. Then, we turn our attention to Pawlak rough sets and consider some of their possible algebraic structures. Finally, we will see that also Fuzzy Sets are a model of the same algebras. Particular attention is given to HW algebra which is a strong and rich structure able to characterize both rough sets and fuzzy sets.

Among the huge number of attributes or features present in real life data sets, only a small fraction of them is effective to represent the data set accurately. Prior to analysis of the data set, selecting or extracting relevant and significant features is an important preprocessing step used for pattern recognition, data mining, and machine learning. In this regard, a novel dimensionality reduction method, based on fuzzy-rough sets, is presented that simultaneously selects attributes and extracts features using the concept of feature significance. The method is based on maximizing both relevance and significance of the reduced feature set, whereby redundancy therein is removed. The paper also presents classical and neighborhood rough sets for computing relevance and significance of the feature set, and compares their performance with that of fuzzy-rough sets based on the predictive accuracy of nearest neighbor rule, support vector machine, and decision tree. An important finding is that the proposed dimensionality reduction method based on fuzzyrough sets is shown to be more effective for generating relevant and significant feature subset. The effectiveness of the proposed fuzzy-rough set based dimensionality reduction method, along with a comparison with existing attribute selection and feature extraction methods, is demonstrated on real life data sets.

In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.

In this paper, we address a class of bilevel multiobjective programming problem where the lower level is a linear multiobjective optimization problem. We use the concepts of satisfactoriness as well as multiobjective optimization at the upper level and a measurement function at the lower level, to develop an interactive method for solving such a problem. The final solution of the proposed method is always efficient to the upper level when the lower level achieves satisfaction. It may be more significant in practice since bilevel programming problem is hierarchical, and the upper level decision making is in the dominant position. Finally, an illustrative numerical example is given to demonstrate the feasibility of the proposed method.

We discuss information granule calculi as a basis of granular computing. They are defined by constructs like information granules, basic relations of inclusion and closeness between information granules as well as operations on them. The exact interpretation between granule languages of different information sources (agents) often does not exist. Hence (rough) inclusion and closeness of granules are considered instead of their equality. Examples of all the basic constructs of information granule calculi are presented. The construction of more complex information granules is described by expressions called terms. We discuss the synthesis problem of robust terms, i.e., descriptions of information granules, satisfying a given specification in a satisfactory degree. We also present a method for synthesis of information granules represented by robust terms (approximate schemes of reasoning) by means of decomposition of specifications for such granules. The discussed problems of granular computing are of special importance for many applications, in particular related to spatial reasoning as well as to knowledge discovery and data mining.

This paper presents an approach in analyzing personality types, temperament and team diversity to determine software engineering (SE) teams performance. The benefits of understanding personality types and its relationships amongst team members are crucial for project success. Rough set analysis was used to analyze Myers-Briggs Type Indicator (MBTI) personality types, Keirsey temperament, team diversity, and team performance. The result shows positive relationships between these attributes.

Attribute reduction is one of the most meaningful research topics in the existing fuzzy rough sets, and the approach of discernibility matrix is the mathematical foundation of computing reducts. When computing reducts with discernibility matrix, we find that only the minimal elements in a discernibility matrix are sufficient and necessary. This fact motivates our idea in this paper to develop a novel algorithm to find reducts that are based on the minimal elements in the discernibility matrix. Relative discernibility relations of conditional attributes are defined and minimal elements in the fuzzy discernibility matrix are characterized by the relative discernibility relations. Then, the algorithms to compute minimal elements and reducts are developed in the framework of fuzzy rough sets. Experimental comparison shows that the proposed algorithms are effective.

Conflict analysis and resolution play an important role in business, governmental, political and lawsuit disputes, labor-management negotiations, military operations and others. Many mathematical models of conflict situations have been proposed and investigated. In this paper a novel approach to conflict analysis, based on rough set theory, is outlined. Basic concepts of this approach are defined and analyzed. An illustration of the introduced concepts by the Middle East conflict is presented.

In this paper, we present the covering rough sets based on neighborhoods by approximation operations as a new type of extended covering rough set models. In fact, we have  introduced generalizations to W. Zhu approaches (Zhu, 2007). Based on the notion of neighborhood induced from any binary relation, four different pairs of dual approximation operators are defined with their properties being discussed. The relationships among these operators are investigated. Finally, an interesting theorem to generate different topologies is provided. Comparisons between these topologies are discussed. In addition, several examples and counter examples to indicate counter connections are investigated.

In virtualization based system load balancing and migration enabled consolidation or dispersal
of virtual machine (VM) is an important issue. The decision of consolidation or dispersal is
taken depending upon the type and amount of load in VMs. Clustering of VMs depends on the
type of loads is a major issue in load balancing of VMs. Analysis of different loads and the
resource usage by them is a major part of this work. For each of the resources there are many
parameters are present to describe the usage of it. Different feature selection algorithms has
been used for analysis of the data and to reduce the dimensionality of the data. The clustering
techniques are mainly applied on this analysed data. K-means and EM algorithm has been used
for clustering of the VMs. The goodness of the clusters has been determined using Davies-
Bouldin index and Dunn index.

Recently machine learning-based Intrusion Detection systems (IDs) have been subjected to extensive researches because they can detect both misuse and anomaly. Most of existing IDs use all features in the network packet to look for known intrusive patterns. In this paper a new hybrid model RSC-PGP (Rough Set Classification - Parallel Genetic Programming) is presented to address the problem of identifying important features in building an intrusion detection system, increase the convergence speed and decrease the training time of RSC. Tests are done on KDD- 99 data used for The Third International Knowledge Discovery and Data Mining Tools Competition. Results showed that the proposed model gives better and robust representation of rules as it was able to select features resulting in great data reduction, time reduction and error reduction in detecting new attacks. Empirical results reveal that Genetic Programming (GP) based techniques could play a major role in developing IDs which are light weight and accurate  hen compared to some of the conventional intrusion detection systems based on machine learning paradigms.

Credit scoring is the term used to describe methods utilized for classifying applicants for credit into classes of risk. This paper evaluates two induction approaches, rough sets and decision trees, as techniques for classifying credit (business) applicants. Inductive learning methods, like rough sets and decision trees, have a better knowledge representational structure than neural networks or statistical procedures because they can be used to derive production rules. If decision trees have already been used for credit granting, the rough sets approach is rarely utilized in this domain. In this paper, we use production rules obtained on a sample of 1102 business loans in order to compare the classification abilities of the two techniques. We show that decision trees obtain better results with 87.5% of good classifications with a pruned tree, against 76.7% for rough sets. However, decision trees make more type-II errors than rough sets, but fewer type-I errors.

Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF-THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models.

Color Image segmentation splits an image into modules, with high correlation among objects contained in the image. Many color image segmentation algorithms in the literature, segment an image on the basis of color, texture and as a combination of both color and texture. In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the Sequential Minimal Optimization-Support Vector Machine (SMO-SVM) classifier for segmentation. Gabor filter decomposition is used for extracting the textural features and homogeneity model is used for obtaining the color features. The SMO-SVM is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set based membership functions efficiently handle the problem of overlapping clusters. The lower and upper approximation concepts of rough sets effectively deal with uncertainty, vagueness, and incompleteness in data. Parameterization tools are not necessary in defining Soft set theory. The goodness aspects of soft sets, rough sets and fuzzy sets are integrated in the proposed algorithm to achieve improved segmentation performance. The proposed algorithm is comparable and achieved better performance compared with the state of the art algorithms found in the literature.

In the design of kansei (emotional) quality, one of the important issues is to extract causal relations between physical design attributes and the customer"s emotional responses. Without such relations, a designer has to rely on his/her own sense that may be different from the customer"s. In this paper, we propose a new method for extraction of logical rules consisting of combinations of design attributes that explain a customer"s emotional judgment towards product appearance. In the method, we apply a reduct calculation in rough set theory to derive alternatives of causal rules between design attributes and emotional judgments, and use the customer"s eye gaze features for refining the rules. We extract two types of visual attentions (VA), i.e., a single visual attention (SVA) and a combinational visual attention (CVA), by using the proposed gaze features. To demonstrate the effectiveness of the method, we conducted a sensory evaluation experiment using a car-interior design as a case study. In the experiment, multiple participants evaluated impressions of multiple design samples by selecting from a set of words. During the experiment, we recorded the participants" eye gaze movements as coordinates on a screen, and asked them to vocalize aloud what they were thinking. After an evaluation of each design sample, we conducted a retrospective interview. From the results, we confirmed that the estimated SVA and CVA significantly covered the vocalized thoughts and statements made in the retrospective interview. The estimated VA reduced 53% of the erroneous causal rules and improved the quality of the rules. We found a case where two participants making the same emotional judgments have implicitly different points of view when evaluating the same design sample. Most conventional causality analysis has been unsuccessful in finding such diversity of points of view.

Uncertainty in service management stems from the incompleteness and vagueness of the conditioning attributes that characterize a service. In particular, location based services often have complex interaction mechanisms in terms of their neighborhood relationships. Classical location service models require rigorous parameters and conditioning attributes and offers limited flexibility to incorporate imprecise or ambiguous evidences. In this paper we have developed a formal model of uncertainty in service management. We have developed a rough set and Dempster-Shafer's evidence theory based formalism to objectively represent uncertainty inherent in the process of service discovery, characterization, and classification. Rough set theory is ideally suited for dealing with limited resolution, vague and incomplete information, while Dempster-Shafer's evidence theory provides a consistent approach to model an expert's belief and ignorance in the classification decision process. Integrating these two formal approaches in spatial domain provides a way to model an expert's belief and ignorance in service classification. In an application scenario of the model we have used a cognitive map of retail site assessment, which reflects the partially subjective assessment process. The uncertainty hidden in the cognitive map can be consistently formalized using the proposed model. Thus we provide a naturalistic means of incorporating both qualitative aspects of intuitive knowledge as well as hard empirical information for service management within a formal uncertainty framework.

This paper presents some remarks about making more reliable aircraft control
and navigation system. On simple examples the author presents the influence of architecture
of a fault tolerant system on its reliability. The proposed description of fault diagnosis
procedures and control reconfiguration enables preparation and analysis of a fault tolerant
system.

One of the main obstacles facing the application of computational intelligence technologies in pattern recognition (and indeed in many other tasks) is that of dataset dimensionality. To enable pattern classifiers to be effective, a dimensionality minimization step is usually carried out beforehand. Rough set theory has been successfully applied for this as it requires only the supplied data and no other information; most other methods require supplementary knowledge. However, the main limitation of traditional rough set-based selection in the literature is the restrictive requirement that all data is discrete; it is not possible to consider real-valued or noisy data. This has been tackled previously via the use of discretization methods, but may result in information loss. This paper investigates two approaches based on rough set extensions, namely fuzzy-rough and tolerance rough sets, that address these problems and retain dataset semantics. The methods are compared experimentally and utilized for the task of forensic glass fragment identification.

The theory of belief functions, sometimes referred to as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, to be later developed by Glenn Shafer as a general framework for modelling epistemic uncertainty. The methodology is now well established as a general framework for reasoning with uncertainty, with well-understood connections to related frameworks such as probability, possibility, random set and imprecise probability theories. Importantly, in recent years the number of papers published on the theory and application of belief functions has been booming (reaching over 800 in 2014 alone), displaying strong growth in particular in the East Asian community and among practitioners working on multi-criteria decision making, earth sciences, and sensor fusion. Belief functions are a natural tool to cope with heavy uncertainty, lack of evidence and missing data, and extremely rare events.

An early debate on the rationale of belief functions gave a strong contribution to the growth and success of the UAI community and series of conference in the Eighties and Nineties, thanks to the contribution of scientists of the caliber of Glenn Shafer, Judea Pearl, Philippe Smets and Prakash Shenoy, among others. Ever since the UAI and BELIEF community have somewhat diverged, and the proposers’ effort has been recently directed towards going back to a closer relationships and exchange of ideas between the two communities. This was one of the aims of the recent BELIEF 2014 International Conference of which the proposers were General Chair and member of the Steering Committee, respectively. A number of books are being published on the subject as we speak, and the impact of the belief function approach to uncertainty is growing.

The tutorial aims at bridging the gap between researchers in the field and the wider AI and Uncertainty Theory community, with the longer term goal of a more fruitful collaboration and dissemination of ideas.

The generation of effective feature-based rules is essential to the development of any intelligent system. This paper presents an approach that integrates a powerful fuzzy rule generation algorithm with a rough set-assisted feature reduction method to generate diagnostic rule with a certainty factor. Certainty factor of each rule is calculated by considering both the membership value of each linguistic term introduced at time of fuzzyfication of data as well as possibility values, due to inconsistent data, generated by rough set theory at time of rule generation. In time of knowledge inferencing in an intelligent system, certainty factor of each rule will play an important role to find out the appropriate rule to be selected. Experimental results demonstrate the superiority of our approach.