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FURIA: An Algorithm For Unordered Fuzzy Rule Induction
"... This paper introduces a novel fuzzy rulebased classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the wellknown RIPPER algorithm, a stateoftheart rule learner, while preserving its advantages, such as simple and comprehensible rule sets ..."
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This paper introduces a novel fuzzy rulebased classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the wellknown RIPPER algorithm, a stateoftheart rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy. 1
Grouping, overlap and generalized bientropic functions for fuzzy modeling of pairwise comparisons
 IEEE Trans. Fuzzy Syst
, 2012
"... Abstract—In this paper, we propose new aggregation functions for the pairwise comparison of alternatives in fuzzy preference modeling. More specifically, we introduce the concept of a grouping function, i.e., a specific type of aggregation function that combines two degrees of support (weak preferen ..."
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Abstract—In this paper, we propose new aggregation functions for the pairwise comparison of alternatives in fuzzy preference modeling. More specifically, we introduce the concept of a grouping function, i.e., a specific type of aggregation function that combines two degrees of support (weak preference) into a degree of information or, say, a degree of comparability between two alternatives, and we relate this new concept to that of incomparability. Grouping functions of this type complement the existing concept of overlap functions in a natural way, since the latter can be used to turn two degrees of weak preference into a degree of indifference. We also define the socalled generalized bientropic functions that allow for a unified representation of overlap and grouping functions. Apart from analyzing mathematical properties of these types of functions and exploring relationships between them, we elaborate on their use in fuzzy preference modeling and decision making. We present an algorithm to elaborate on an alternative preference ranking that penalizes those alternatives for which the expert is not sure of his/her preference. Index Terms—Decision making, generalized bientropic function, grouping function, incomparability, overlap function, pairwise comparison, preference relations. I.
A first study on the noise impact in classes for fuzzy rule based classification systems
 in: Proceedings of the 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE’10), IEEE
, 2010
"... Abstract—The presence of noise is common in any real data set and may adversely affect the accuracy, construction time and complexity of the classifiers. Models built by Fuzzy Rule Based Classification Systems are recognised for their interpretability, but traditionally these methods have not consid ..."
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Abstract—The presence of noise is common in any real data set and may adversely affect the accuracy, construction time and complexity of the classifiers. Models built by Fuzzy Rule Based Classification Systems are recognised for their interpretability, but traditionally these methods have not considered the presence of noise in the data, so it would be interesting to quantify its effect on them. The aim of this contribution is to study the behavior and robustness of Fuzzy Rule Based Classification Systems in presence of noise. In order to do this, 69 synthetic data sets have been created from 23 data sets from the UCI repository. Different levels of noise have been introduced artificially in the class in order to analyze the FRBCSs when noise is present. The methods of Chi et al. and PDFC have been considered as a case study, analyzing the accuracy of the models created. From the results obtained, it is possible to deduce that Fuzzy Rule Based Classification Systems have a good tolerance to class noise. I.
Fuzzy Rule Based Classification Systems versus Crisp Robust Learners Trained in Presence of Class Noise’s Effects: a Case of
 Study, IEEE, 11th International Conference on Intelligent Systems Design and Applications
, 2011
"... Abstract—The presence of noise is common in any realworld dataset and may adversely affect the accuracy, construction time and complexity of the classifiers in this context. Traditionally, many algorithms have incorporated mechanisms to deal with noisy problems and reduce noise’s effects on perform ..."
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Abstract—The presence of noise is common in any realworld dataset and may adversely affect the accuracy, construction time and complexity of the classifiers in this context. Traditionally, many algorithms have incorporated mechanisms to deal with noisy problems and reduce noise’s effects on performance; they are called robust learners. The C4.5 crisp algorithm is a wellknown example of this group of methods. On the other hand, models built by Fuzzy Rule Based Classification Systems are widely recognized for their robustness to imperfect data, but also for their interpretability. The aim of this contribution is to analyze the good behavior and robustness of Fuzzy Rule Based Classification Systems when noise is present in the examples ’ class labels, especially versus robust learners. In order to accomplish this study, a large number of datasets are created by introducing different levels of noise into the class labels in the training sets. We compare a Fuzzy Rule Based Classification System, the Fuzzy Unordered Rule Induction Algorithm, with respect to the C4.5 classic robust learner which is considered tolerant to noise. From the results obtained it is possible to observe that Fuzzy Rule Based Classification Systems have a good tolerance, in comparison to the C4.5 algorithm, to class noise.
Classification of Imprecise Data Using Interval
"... In this paper, an imprecise data classification is considered using new version of Fisher discriminator, namely interval Fisher. In the conventional formulation of Fisher, elements of withinclass scatter matrix (related to covariance matrix between clusters) and betweenclass scatter matrix (relat ..."
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In this paper, an imprecise data classification is considered using new version of Fisher discriminator, namely interval Fisher. In the conventional formulation of Fisher, elements of withinclass scatter matrix (related to covariance matrix between clusters) and betweenclass scatter matrix (related to covariance matrix of centers of clusters) have single values; but in the interval Fisher, the elements of matrices are in the interval form and can vary in a range. The particle swarm optimization search method is used for solving a constrained optimization problem of the interval Fisher discriminator. Unlike conventional Fisher with one optimal hyperplane, interval Fisher gives two optimal hyperplanes thereupon three decision regions are obtained. Two classes with regard to imprecise scatter matrices are derived by decision making using these optimal hyperplanes. Also, fuzzy region lets us help in fuzzy decision over input test samples. Unlike a support vector classifier with two parallel hyperplanes, interval Fisher generally gives us two nonparallel hyperplanes. Experimental results show the suitability of this idea. C © 2011 Wiley Periodicals, Inc. 1.
An Extraction Method for the Characterization of the Fuzzy Rule Based Classification Systems ’ Behavior using Data Complexity Measures: A case of study with FHGBML
"... Abstract — When dealing with problems using Fuzzy Rule Based Classification Systems it is difficult to know in advance whether the model will perform well or badly. In this work we present an automatic extraction method to determine the domains of competence of Fuzzy Rule Based Classification System ..."
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Abstract — When dealing with problems using Fuzzy Rule Based Classification Systems it is difficult to know in advance whether the model will perform well or badly. In this work we present an automatic extraction method to determine the domains of competence of Fuzzy Rule Based Classification Systems As a case of study we use the Fuzzy Hybrid Genetic Based Machine Learning method. We consider twelve metrics of data complexity in order to analyze the behavior patterns of this method, obtaining intervals of such data complexity measures with good or bad performance of it. Combining these intervals we obtain rules that describe both good or bad behaviors of the Fuzzy Rule Based Classification System mentioned. These rules allow describe both good or bad behaviors of the Fuzzy Rule Based Classification Systems mentioned, allowing us to characterize the response quality of the methods from the data set complexity metrics of a given data set. Thus, we can establish the domains of competence of the Fuzzy Rule Based Classification Systems considered, making it possible to establish when the method will perform well or badly prior to its application.
Fuzzy Classifier: On the Synergy Between nDimensional Overlap Functions and Decomposition Strategies
"... Abstract—There are many realworld classification problems involving multiple classes, e.g., in bioinformatics, computer vision, or medicine. These problems are generally more difficult than their binary counterparts. In this scenario, decomposition strategies usually improve the performance of cl ..."
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Abstract—There are many realworld classification problems involving multiple classes, e.g., in bioinformatics, computer vision, or medicine. These problems are generally more difficult than their binary counterparts. In this scenario, decomposition strategies usually improve the performance of classifiers. Hence, in this paper, we aim to improve the behavior of fuzzy association rulebased classification model for highdimensional problems (FARCHD) fuzzy classifier in multiclass classification problems using decomposition strategies, and more specifically OneversusOne (OVO) and OneversusAll (OVA) strategies. However, when these strategies are applied on FARCHD, a problem emerges due to the lowconfidence values provided by the fuzzy reasoning method. This undesirable condition comes from the application of the product tnorm when computing the matching and association degrees, obtaining low values, which are also dependent on the number of antecedents of
Enhancing Fuzzy Rule Based Systems in MultiClassification Using Pairwise Coupling with Preference Relations
"... This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multiclassifier approach using fuzzy preference relations for dealing with multiclass classification. The idea is to decompose the original dataset into binary classification problems using a ..."
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This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multiclassifier approach using fuzzy preference relations for dealing with multiclass classification. The idea is to decompose the original dataset into binary classification problems using a pairwise coupling approach (confronting all pair of classes), and to obtain a fuzzy system for each one of them. Along the inference process, each FRBCS generates an association degree for its two classes, and these values are encoded into a fuzzy preference relation. The final class of the whole FRBCS will be obtained by decision making following a nondominance criterium. We show the goodness of our proposal in contrast with the base fuzzy model with an extensive experimental study following a statistical study for analysing the differences in performance among the algorithms. We will also contrast our results versus the wellknown C4.5 decision tree.
FR3: A Fuzzy Rule Learner for Inducing Reliable Classifiers
"... This paper introduces a fuzzy rulebased classification method called FR3, which is short for Fuzzy Round Robin RIPPER. In the context of polychotomous classification, it uses a fuzzy extension of the wellknown RIPPER algorithm as a base learner within a round robin scheme. A key feature of FR3 is ..."
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This paper introduces a fuzzy rulebased classification method called FR3, which is short for Fuzzy Round Robin RIPPER. In the context of polychotomous classification, it uses a fuzzy extension of the wellknown RIPPER algorithm as a base learner within a round robin scheme. A key feature of FR3 is its ability to represent different facets of uncertainty involved in a classification decision in a more faithful way, thereby providing the basis for implementing “reliable classifiers ” that may, for example, abstain from a decision when not being sure enough. 1