Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule

An interesting feature of encoding the individuals of a Pittsburgh learning classifier system as a decision list is the emergent generation of a default rule. However, performance of the system is strongly tied to the learning system choosing the correct class for this default rule. In this paper we experimentally study the use of an explicit (static) default rule. We first test simple policies for setting the class of the default rule, such as the majority/minority class of the problem. Next, we introduce some techniques to automatically determine the most suitable class.

[1]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

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

[3]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

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

[5]  David E. Goldberg,et al.  Sizing Populations for Serial and Parallel Genetic Algorithms , 1989, ICGA.

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

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 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]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[10]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[11]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[12]  Terence Soule,et al.  Effects of Code Growth and Parsimony Pressure on Populations in Genetic Programming , 1998, Evolutionary Computation.

[13]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[14]  Using genetic algorithms for concept learning , 1993, Machine Learning.

[15]  Jaume Bacardit,et al.  Analysis and Improvements of the Adaptive Discretization Intervals Knowledge Representation , 2004, GECCO.

[16]  Jaume Bacardit Peñarroya.  Pittsburgh genetic-based machine learning in the data mining era: representations, generalization, and run-time , 2004 .

[17]  R. Rivest.  Learning Decision Lists , 1987, Machine Learning.

[18]  Jaume Bacardit,et al.  Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System , 2005, IWLCS.