Feature construction and selection using genetic programming and a genetic algorithm

Smith, Matthew G.; Bull, Larry

Abstract

The use of machine learning techniques to automatically analyse data for information is becoming increasingly widespread. In this paper we examine the use of Genetic Programming and a Genetic Algorithm to pre-process data before it is classified using the C4.5 decision tree learning algorithm. The Genetic Programming is used to construct new features from those available in the data, a potentially significant process for data mining since it gives consideration to hidden relationships between features. The Genetic Algorithm is used to determine which such features are the most predictive. Using ten well-known datasets we show that our approach, in comparison to C4.5 alone, provides marked improvement in a number of cases.

Citation

Smith, M. G., & Bull, L. (2003). Feature construction and selection using genetic programming and a genetic algorithm. Lecture Notes in Artificial Intelligence, 2610, 229-237. https://doi.org/10.1007/3-540-36599-0_21

• Journal Article Type: Conference Paper
• Publication Date: Jan 1, 2003
• Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Print ISSN: 0302-9743
• Electronic ISSN: 1611-3349
• Publisher: Springer Verlag
• Peer Reviewed: Not Peer Reviewed
• Volume: 2610
• Pages: 229-237
• Series Title: Lecture Notes in Computer Science
• Series Number: 2610
• ISBN: 354000971X; 9783540009719
• DOI: https://doi.org/10.1007/3-540-36599-0_21
• Keywords: programming techniques, computation by abstract devices, algorithm analysis and problem complexity, artificial intelligence, pattern recognition, bioinformatics
• Public URL: https://uwe-repository.worktribe.com/output/1074750
• Publisher URL: http://dx.doi.org/10.1007/3-540-36599-0_21