Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
 Igor Kononenko,
 Edvard Šimec,
 Marko RobnikŠikonja
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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.
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 Title
 Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
 Journal

Applied Intelligence
Volume 7, Issue 1 , pp 3955
 Cover Date
 19970101
 DOI
 10.1023/A:1008280620621
 Print ISSN
 0924669X
 Online ISSN
 15737497
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 learning from examples
 estimating attributes
 impurity function
 RELIEFF
 empirical evaluation
 Industry Sectors
 Authors

 Igor Kononenko ^{(1)}
 Edvard Šimec ^{(1)}
 Marko RobnikŠikonja ^{(1)}
 Author Affiliations

 1. University of Ljubljana, Tržaška 25, SI1001, Ljubljana, Slovenia Email