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Overcoming the myopia of inductive learning algorithms with RELIEFF (1997)
| Venue: | Applied Intelligence |
| Citations: | 54 - 14 self |
BibTeX
@ARTICLE{Kononenko97overcomingthe,
author = {Igor Kononenko and Edvard Simec and Marko Robnik- Sikonja},
title = {Overcoming the myopia of inductive learning algorithms with RELIEFF},
journal = {Applied Intelligence},
year = {1997},
volume = {7},
pages = {39--55}
}
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Abstract
. Current inductive machine learning algorithms typically use greedy search with limited lookahead. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of myopic impurity functions and lookahead, we propose to use RELIEFF, an extension of RELIEF developed by Kira and Rendell [10], [11], for heuristic guidance of inductive learning algorithms. We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step. The algorithm is tested on several artificial and several real world problems and the results are compared with some other well known machine learning algorithms. Excellent results on artificial data sets and two real world problems show the advantage of the presented approach to inductive learning. Keywords: learning from examples, estimating attributes, impurity function, RELIEFF, empirical evaluation 1. Introduction ...
Keyphrases
current inductive machine presented approach significant conditional dependency selection step decision tree heuristic guidance greedy search impurity function limited lookahead describe training object inductive learning algorithm top induction myopic impurity function several real world problem artificial data set excellent result empirical evaluation real world problem
