Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 2, Pages 3401-3406Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.02.010
Keywords
Feature construction; Machine learning
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Inductive learning algorithms, in general, perform well oil 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 ally 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. (C) 2008 Elsevier Ltd. All rights reserved.
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