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Iterative feature construction for improving inductive learning algorithms
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DOI：10.1016/j.eswa.2008.02.010.png)
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Abstract
En 中文
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.
Keywords:
Feature construction
Machine learning
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