期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 3401-3406出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.02.010
关键词
Feature construction; Machine learning
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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