Feature subset selection for rule induction using RIPPER

Many existing rule learning systems perform poorly on large noisy datasets because of the presence of irrelevant features. In this paper we propose a hybrid approach combining Genetic Algorithms and RIPPER, to design better classifiers. Our experiments with several benchmark datasets show the feasibility of this approach.

[1]  Vasant Honavar,et al.  Analysis of Neurocontrollers Designed by Simulated Evolution , 1995 .

[2]  Pat Langley,et al.  Elements of Machine Learning , 1995 .

[3]  Ralph L. Keeney,et al.  Decisions with multiple objectives: preferences and value tradeoffs , 1976 .

[4]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[5]  Raymond J. Mooney,et al.  An Experimental Comparison of Symbolic and Connectionist Learning Algorithms , 1989, IJCAI.

[6]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[8]  Vasant Honavar,et al.  Power System Security Margin Prediction Using Radial Basis Function Networks , 1997 .

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

[10]  Johannes Fürnkranz,et al.  Incremental Reduced Error Pruning , 1994, ICML.

[11]  Vasant Honavar,et al.  Properties of Genetic Representations of Neural Architectures. , 1995 .

[12]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[13]  William W. Cohen.  Fast Eeective Rule Induction , 1995 .

[14]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

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

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

[17]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[18]  Vasant Honavar,et al.  Mobile Intelligent Agents for Document Classification and Retrieval: A Machine Learning Approach , 1998 .

[19]  Daphne Koller,et al.  Hierarchically Classifying Documents Using Very Few Words , 1997, ICML.

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

[21]  Kevin Tomsovic,et al.  Power System Security , 1999 .