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64
Ten years of genetic fuzzy systems: current framework and new trends
, 2004
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Designing fuzzy inference systems from data: an interpretabilityoriented review
 IEEE Trans. Fuzzy Systems
"... Abstract—Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules infer ..."
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Cited by 72 (13 self)
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Abstract—Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for humancomputer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. This paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined. Index Terms—Fuzzy inference systems, fuzzy partitioning, interpretability, rule induction, system optimization. I.
Effect of rule weights in fuzzy rulebased classification systems
 IEEE Transactions on Fuzzy Systems
, 2001
"... Abstract—This paper examines the effect of rule weights in fuzzy rulebased classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification ..."
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Cited by 69 (13 self)
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Abstract—This paper examines the effect of rule weights in fuzzy rulebased classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility grade with the new pattern. When we use fuzzy IF–THEN rules with certainty grades (i.e., rule weights), the winner is determined as the rule with the maximum product of the compatibility grade and the certainty grade. In this paper, the effect of rule weights is illustrated by drawing classification boundaries using fuzzy IF–THEN rules with/without certainty grades. It is also shown that certainty grades play an important role when a fuzzy rulebased classification system is a mixture of general rules and specific rules. Through computer simulations, we show that comprehensible fuzzy rulebased systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when we use fuzzy IF–THEN rules with certainty grades. Index Terms—Fuzzy reasoning, fuzzy rulebased systems, pattern classification, rule extraction. I.
Generating the knowledge base of a fuzzy rulebased system by the genetic learning of the data base
 IEEE Tran. Fuzzy Systems
"... Abstract—A new method is proposed to automatically learn the knowledge base (KB) by finding an appropiate data base (DB) by means of a genetic algorithm while using a simple generation method to derive the rule base (RB). Our genetic process learns the number of linguistic terms per variable and the ..."
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Cited by 38 (14 self)
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Abstract—A new method is proposed to automatically learn the knowledge base (KB) by finding an appropiate data base (DB) by means of a genetic algorithm while using a simple generation method to derive the rule base (RB). Our genetic process learns the number of linguistic terms per variable and the membership function parameters that define their semantics, while a rule base generation method learns the number of rules and their composition. Index Terms—Fuzzy rulebased systems, data base, learning, genetic algorithms. I.
Genetic Feature Selection in a Fuzzy RuleBased Classification System Learning Process for High Dimensional Problems
, 2000
"... The inductive learning of a Fuzzy RuleBased Classification System (FRBCS) is made difficult by the presence of a high feature number that increases the dimensionality of the problem being solved. The difficulty comes from the exponential growth of the fuzzy rule search space with the increase in th ..."
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Cited by 23 (8 self)
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The inductive learning of a Fuzzy RuleBased Classification System (FRBCS) is made difficult by the presence of a high feature number that increases the dimensionality of the problem being solved. The difficulty comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered in the learning process. In this work, we present a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more efficient way, FRBCSs composed of a set of comprehensible fuzzy rules with high classification ability. The proposed process fixes, a priori, the number of selected features, and therefore, the size of the search space of candidate fuzzy rules. The experimentation carried out, using Sonar example base, shows a significant improvement on simplicity, precision and efficiency achieved by adding the proposed feature selection processes to the multistage genetic learning method or to other learning methods.
Flexible neurofuzzy systems
 IEEE TRANS. NEURAL NETW
, 2003
"... In this paper, we derive new neurofuzzy structures called flexible neurofuzzy inference systems or FLEXNFIS. Based on the input–output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to f ..."
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Cited by 23 (6 self)
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In this paper, we derive new neurofuzzy structures called flexible neurofuzzy inference systems or FLEXNFIS. Based on the input–output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certainty weights to aggregation of rules and to connectives of antecedents; and 3) parameterized families of Tnorms and Snorms to fuzzy implication operators, to aggregation of rules and to connectives of antecedents. Our approach introduces more flexibility to the structure and design of neurofuzzy systems. Through computer simulations, we show that Mamdanitype systems are more suitable to approximation problems, whereas logicaltype systems may be preferred for classification problems.
Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm
, 2001
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A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in FuzzyRule Based Classification Systems
"... In this contribution, we propose a genetic process to select an appropiate set of features in a Fuzzy RuleBased Classification System (FRBCS) and to automatically learn the whole Data Base definition. An adhoc data covering learning method is considered to obtain the Rule Base. The method uses a m ..."
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Cited by 12 (9 self)
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In this contribution, we propose a genetic process to select an appropiate set of features in a Fuzzy RuleBased Classification System (FRBCS) and to automatically learn the whole Data Base definition. An adhoc data covering learning method is considered to obtain the Rule Base. The method uses a multiobjective genetic algorithm in order to obtain a good balance between accuracy and interpretability.
Evolutionary fuzzy rule induction process for subgroup discovery: A case study in marketing
 Transactions on Fuzzy Systems
, 2007
"... Abstract—This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rule allows us to represent knowledge about ..."
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Cited by 11 (1 self)
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Abstract—This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragón, Spain, in which it is necessary to extract automatically relevant and interesting information that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts. Index Terms—Data mining, descriptive induction, evolutionary algorithms, genetic fuzzy systems, subgroup discovery. I.