Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
388234 | Expert Systems with Applications | 2009 | 6 Pages |
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.
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Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Selwyn Piramuthu, Riyaz T. Sikora,