Genetic programming with a genetic algorithm for feature construction and selection

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 primarily 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. 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. A 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. We then examine its use with other well-known machine learning techniques. © 2005 Springer Science + Business Media, Inc.

• Journal Article Type: Article
• Publication Date: Sep 1, 2005
• Journal: Genetic Programming and Evolvable Machines
• Print ISSN: 1389-2576
• Electronic ISSN: 1573-7632
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Not Peer Reviewed
• Volume: 6
• Issue: 3
• Pages: 265-281
• DOI: https://doi.org/10.1007/s10710-005-2988-7
• Keywords: genetic programming, genetic algorithm, feature construction, feature selection, classification, machine learning
• Public URL: https://uwe-repository.worktribe.com/output/1056232
• Publisher URL: http://dx.doi.org/10.1007/s10710-005-2988-7