The Experts below are selected from a list of 36156 Experts worldwide ranked by ideXlab platform
Sam Kwong - One of the best experts on this subject based on the ideXlab platform.
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agent based evolutionary approach for interpretable rule based knowledge extraction
Systems Man and Cybernetics, 2005Co-Authors: Hanli Wang, Sam KwongAbstract:An agent-based evolutionary approach is proposed to extract interpretable rule-based knowledge. In the multiagent system, each fuzzy set agent autonomously determines its own fuzzy sets information, such as the number and distribution of the fuzzy sets. It can further consider the Interpretability of fuzzy systems with the aid of hierarchical chromosome formulation and Interpretability-based regulation method. Based on the obtained fuzzy sets, the Pittsburgh-style approach is applied to extract fuzzy rules that take both the accuracy and Interpretability of fuzzy systems into consideration. In addition, the fuzzy set agents can cooperate with each other to exchange their fuzzy sets information and generate offspring agents. The parent agents and their offspring compete with each other through the arbitrator agent based on the criteria associated with the accuracy and Interpretability to allow them to remain competitive enough to move into the next population. The performance with emphasis upon both the accuracy and Interpretability based on the agent-based evolutionary approach is studied through some benchmark problems reported in the literature. Simulation results show that the proposed approach can achieve a good tradeoff between the accuracy and Interpretability of fuzzy systems.
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multi objective hierarchical genetic algorithm for interpretable fuzzy rule based knowledge extraction
Fuzzy Sets and Systems, 2005Co-Authors: Hanli Wang, Sam KwongAbstract:A new scheme based on multi-objective hierarchical genetic algorithm (MOHGA) is proposed to extract interpretable rule-based knowledge from data. The approach is derived from the use of multiple objective genetic algorithm (MOGA), where the genes of the chromosome are arranged into control genes and parameter genes. These genes are in a hierarchical form so that the control genes can manipulate the parameter genes in a more effective manner. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. Some important concepts about the Interpretability are introduced and the fitness function in the MOGA will consider both the accuracy and Interpretability of the fuzzy model. In order to remove the redundancy of the rule base proactively, we further apply an Interpretability-driven simplification method to newborn individuals. In our approach, we first apply the fuzzy clustering to generate an initial rule-based model. Then the multi-objective hierarchical genetic algorithm and the recursive least square method are used to obtain the optimized fuzzy models. The accuracy and the Interpretability of fuzzy models derived by this approach are studied and presented in this paper. We compare our work with other methods reported in the literature on four examples: a synthetic nonlinear dynamic system, a nonlinear static system, the Lorenz system and the Mackey-Glass system. Simulation results show that the proposed approach is effective and practical in knowledge extraction.
Hanli Wang - One of the best experts on this subject based on the ideXlab platform.
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agent based evolutionary approach for interpretable rule based knowledge extraction
Systems Man and Cybernetics, 2005Co-Authors: Hanli Wang, Sam KwongAbstract:An agent-based evolutionary approach is proposed to extract interpretable rule-based knowledge. In the multiagent system, each fuzzy set agent autonomously determines its own fuzzy sets information, such as the number and distribution of the fuzzy sets. It can further consider the Interpretability of fuzzy systems with the aid of hierarchical chromosome formulation and Interpretability-based regulation method. Based on the obtained fuzzy sets, the Pittsburgh-style approach is applied to extract fuzzy rules that take both the accuracy and Interpretability of fuzzy systems into consideration. In addition, the fuzzy set agents can cooperate with each other to exchange their fuzzy sets information and generate offspring agents. The parent agents and their offspring compete with each other through the arbitrator agent based on the criteria associated with the accuracy and Interpretability to allow them to remain competitive enough to move into the next population. The performance with emphasis upon both the accuracy and Interpretability based on the agent-based evolutionary approach is studied through some benchmark problems reported in the literature. Simulation results show that the proposed approach can achieve a good tradeoff between the accuracy and Interpretability of fuzzy systems.
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multi objective hierarchical genetic algorithm for interpretable fuzzy rule based knowledge extraction
Fuzzy Sets and Systems, 2005Co-Authors: Hanli Wang, Sam KwongAbstract:A new scheme based on multi-objective hierarchical genetic algorithm (MOHGA) is proposed to extract interpretable rule-based knowledge from data. The approach is derived from the use of multiple objective genetic algorithm (MOGA), where the genes of the chromosome are arranged into control genes and parameter genes. These genes are in a hierarchical form so that the control genes can manipulate the parameter genes in a more effective manner. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. Some important concepts about the Interpretability are introduced and the fitness function in the MOGA will consider both the accuracy and Interpretability of the fuzzy model. In order to remove the redundancy of the rule base proactively, we further apply an Interpretability-driven simplification method to newborn individuals. In our approach, we first apply the fuzzy clustering to generate an initial rule-based model. Then the multi-objective hierarchical genetic algorithm and the recursive least square method are used to obtain the optimized fuzzy models. The accuracy and the Interpretability of fuzzy models derived by this approach are studied and presented in this paper. We compare our work with other methods reported in the literature on four examples: a synthetic nonlinear dynamic system, a nonlinear static system, the Lorenz system and the Mackey-Glass system. Simulation results show that the proposed approach is effective and practical in knowledge extraction.
Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.
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Interpretability of linguistic fuzzy rule based systems an overview of Interpretability measures
Information Sciences, 2011Co-Authors: Maria Jose Gacto, Rafael Alcala, Francisco HerreraAbstract:Linguistic fuzzy modelling, developed by linguistic fuzzy rule-based systems, allows us to deal with the modelling of systems by building a linguistic model which could become interpretable by human beings. Linguistic fuzzy modelling comes with two contradictory requirements: Interpretability and accuracy. In recent years the interest of researchers in obtaining more interpretable linguistic fuzzy models has grown. Whereas the measures of accuracy are straightforward and well-known, Interpretability measures are difficult to define since Interpretability depends on several factors; mainly the model structure, the number of rules, the number of features, the number of linguistic terms, the shape of the fuzzy sets, etc. Moreover, due to the subjectivity of the concept the choice of appropriate Interpretability measures is still an open problem. In this paper, we present an overview of the proposed Interpretability measures and techniques for obtaining more interpretable linguistic fuzzy rule-based systems. To this end, we will propose a taxonomy based on a double axis: ''Complexity versus semantic Interpretability'' considering the two main kinds of measures; and ''rule base versus fuzzy partitions'' considering the different components of the knowledge base to which both kinds of measures can be applied. The main aim is to provide a well established framework in order to facilitate a better understanding of the topic and well founded future works.
Antonio Peregrin - One of the best experts on this subject based on the ideXlab platform.
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a multi objective evolutionary algorithm with an Interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification
IEEE International Conference on Fuzzy Systems, 2010Co-Authors: Antonio A Marquez, Francisco Alfredo Marquez, Antonio PeregrinAbstract:In this paper we propose a multi-objective evolutionary algorithm with a mechanism to improve the Interpretability in the sense of complexity for Linguistic Fuzzy Rule based Systems with adaptive defuzzification. The use of parameters in the defuzzification operator introduces a series of values or associated weights to each rule, which improves the accuracy but increases the system complexity and therefore has an effect on the system Interpretability. To this end, we use maximizing the accuracy as an usual objective for the evolutionary process, and we define objectives related with Interpretability, using three metrics: minimizing the classical number of rules, the number of rules with weights associated and the average number of rules triggered by each example. The proposed method was compared in an experimental study with a single objective accuracy-guided algorithm in two real problems showing that many solutions in the Pareto front dominate those obtained by the single objective-based one.
Maria Jose Gacto - One of the best experts on this subject based on the ideXlab platform.
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Interpretability of linguistic fuzzy rule based systems an overview of Interpretability measures
Information Sciences, 2011Co-Authors: Maria Jose Gacto, Rafael Alcala, Francisco HerreraAbstract:Linguistic fuzzy modelling, developed by linguistic fuzzy rule-based systems, allows us to deal with the modelling of systems by building a linguistic model which could become interpretable by human beings. Linguistic fuzzy modelling comes with two contradictory requirements: Interpretability and accuracy. In recent years the interest of researchers in obtaining more interpretable linguistic fuzzy models has grown. Whereas the measures of accuracy are straightforward and well-known, Interpretability measures are difficult to define since Interpretability depends on several factors; mainly the model structure, the number of rules, the number of features, the number of linguistic terms, the shape of the fuzzy sets, etc. Moreover, due to the subjectivity of the concept the choice of appropriate Interpretability measures is still an open problem. In this paper, we present an overview of the proposed Interpretability measures and techniques for obtaining more interpretable linguistic fuzzy rule-based systems. To this end, we will propose a taxonomy based on a double axis: ''Complexity versus semantic Interpretability'' considering the two main kinds of measures; and ''rule base versus fuzzy partitions'' considering the different components of the knowledge base to which both kinds of measures can be applied. The main aim is to provide a well established framework in order to facilitate a better understanding of the topic and well founded future works.
