Exploiting a coevolutionary approach to concurrently select training instances and learn rule bases of Mamdani fuzzy systems

When applied to high dimensional datasets, multi-objective evolutionary learning (MOEL) of fuzzy rule-based systems suffers from high computational costs, mainly due to the fitness evaluation. To use a reduced training set (TS) in place of the overall TS could considerably lessen the required effort. How this reduction should be performed, especially in the context of regression, is still an open issue. In this paper, we propose to adopt a co-evolutionary approach. In the execution of the MOEL, periodically, a single-objective genetic algorithm (SOGA) evolves a population of reduced TSs. The SOGA aims to maximize a purposely-defined index which measures how much a reduced TS is representative of the overall TS in the context of the MOEL. We tested our approach on a real world high dimensional dataset. We show that the Pareto fronts generated by applying the MOEL with the overall and the reduced TSs are comparable, although the use of the reduced TS allows saving on average the 75% of the execution time.

[1]  Hisao Ishibuchi,et al.  Effects of Data Reduction on the Generalization Ability of Parallel Distributed Genetic Fuzzy Rule Selection , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[2]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[3]  Beatrice Lazzerini,et al.  On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems , 2011, Appl. Soft Comput..

[4]  Francisco Herrera,et al.  A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems , 2009, IEEE Transactions on Fuzzy Systems.

[5]  Huan Liu,et al.  Instance Selection and Construction for Data Mining , 2001 .

[6]  Francisco Herrera,et al.  A Multi-Objective Genetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems , 2007, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[7]  Beatrice Lazzerini,et al.  Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets , 2010, Soft Comput..

[8]  Francisco Herrera,et al.  Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability , 2007, Data Knowl. Eng..

[9]  Hannu Koivisto,et al.  A Dynamically Constrained Multiobjective Genetic Fuzzy System for Regression Problems , 2010, IEEE Transactions on Fuzzy Systems.

[10]  Beatrice Lazzerini,et al.  Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems , 2009, Evol. Intell..

[11]  Francisco Herrera,et al.  Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study , 2003, IEEE Trans. Evol. Comput..

[12]  Francisco Herrera,et al.  Evolutionary parallel and gradually distributed lateral tuning of fuzzy rule-based systems , 2009, Evol. Intell..

[13]  Francisco Alfredo Márquez,et al.  Parallel Distributed Two-Level Evolutionary Multiobjective Methodology for Granularity Learning and Membership Functions Tuning in Linguistic Fuzzy Systems , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[14]  Francisco Herrera,et al.  Integration of an Index to Preserve the Semantic Interpretability in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems , 2010, IEEE Transactions on Fuzzy Systems.

[15]  Francisco Herrera,et al.  Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems , 2008, Soft Comput..

[16]  Francisco Herrera,et al.  On the Use of Distributed Genetic Algorithms for the Tuning of Fuzzy Rule Based-Systems , 2010, Parallel and Distributed Computational Intelligence.

[17]  Francisco Herrera,et al.  Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..

[18]  Beatrice Lazzerini,et al.  Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework , 2009, Int. J. Approx. Reason..

[19]  Hisao Ishibuchi,et al.  Multiobjective Genetic Fuzzy Systems , 2009 .

[20]  Alessio Botta,et al.  Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index , 2008, Soft Comput..

[21]  Hisao Ishibuchi,et al.  Parallel distributed genetic fuzzy rule selection , 2008, Soft Comput..

[22]  Hisao Ishibuchi,et al.  Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining , 2004, Fuzzy Sets Syst..

[23]  Beatrice Lazzerini,et al.  A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems , 2007, Soft Comput..

[24]  Francisco Herrera,et al.  Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.