George H. John, Ron Kohavi, Karl Pfleger  Home/Search
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Abstract: We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection... (Update)
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BibTeX entry: (Update)
John, G.H., Kohavi, R., Pfleger, K., Irrelevant Features and the Subset Selection Problem, Proc. of the 11th International Conference on Machine Learning ICML94, pp. 121---129, 1994. http://citeseer.ist.psu.edu/john94irrelevant.html More
@inproceedings{ john94irrelevant,
author = "George H. John and Ron Kohavi and Karl Pfleger",
title = "Irrelevant Features and the Subset Selection Problem",
booktitle = "International Conference on Machine Learning",
pages = "121-129",
note = "Journal version in AIJ, available at http://citeseer.nj.nec.com/13663.html",
year = "1994",
url = "citeseer.ist.psu.edu/john94irrelevant.html" }
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