Bounding the Effect of Noise in Multiobjective Learning Classifier Systems

This paper analyzes the impact of using noisy data sets in Pittsburgh-style learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains. For this reason, we developed a theoretical model for predicting theminimal achievable error in noisy domains. Combining this theoretical model for crisp learners with graphical representations of the evolved hypotheses through multiobjective techniques, we are able to bound the behavior of a learning classifier system. This kind of modeling lets us identify relevant characteristics of the evolved hypotheses, such as overfitting conditions that lead to hypotheses that poorly generalize the concept to be learned.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Stephen F. Smith,et al.  Flexible Learning of Problem Solving Heuristics Through Adaptive Search , 1983, IJCAI.

[3]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Larry J. Eshelman,et al.  Using Weighted Networks to Represent Classification Knowledge in Noisy Domains , 1988, ML.

[6]  Kenneth A. De Jong,et al.  Learning Concept Classification Rules Using Genetic Algorithms , 1991, IJCAI.

[7]  C. Janikow.  Inductive learning of decision rules from attribute-based examples: a knowledge-intensive genetic algorithm approach , 1992 .

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Walter Alden Tackett,et al.  Recombination, selection, and the genetic construction of computer programs , 1994 .

[10]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[11]  Thomas Bck.  Generalized convergence models for tournament|and (1; ?)|selection , 1995 .

[12]  Stewart W. Wilson.  Classifier Fitness Based on Accuracy , 1995, Evolutionary Computation.

[13]  Thomas Bäck,et al.  Generalized Convergence Models for Tournament- and (mu, lambda)-Selection , 1995, ICGA.

[14]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[15]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[16]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[17]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[18]  T. Kovacs.  Deletion Schemes for Classi er Systems , 1999 .

[19]  J. Ross Quinlan,et al.  Simplifying decision trees , 1987, Int. J. Hum. Comput. Stud..

[20]  T. Kovacs.  Deletion schemes for classifier systems , 1999 .

[21]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[22]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[23]  Xavier Llorà,et al.  XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining , 2001, IWLCS.

[24]  Ester Bernadó-Mansilla,et al.  MOLeCS: Using Multiobjective Evolutionary Algorithms for Learning , 2001, EMO.

[25]  D. Goldberg,et al.  Minimal Achievable Error in the LED problem , 2002 .

[26]  David E. Goldberg,et al.  Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection , 2002, IWLCS.

[27]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[28]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.