Iterative feature construction for improving inductive learning algorithms

Authors:
Selwyn Piramuthu;Riyaz T. Sikora
Affiliations:
University of Florida, Information Systems and Operations Management, Gainesville, FL 32611-7169, United States;University of Texas at Arlington, Information Systems and Operations Management, Arlington, TX 6019-0437, United States
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Inductive learning algorithms, in general, perform well on data that have been pre-processed to reduce complexity. By themselves they are not particularly effective in reducing data complexity while learning difficult concepts. Feature construction has been shown to reduce complexity of space spanned by input data. In this paper, we present an iterative algorithm for enhancing the performance of any inductive learning process through the use of feature construction as a pre-processing step. We apply the procedure on three learning methods, namely genetic algorithms, C4.5 and lazy learner, and show improvement in performance.