Greedy Attribute Selection (1994)
| Venue: | In Proceedings of the Eleventh International Conference on Machine Learning |
| Citations: | 172 - 2 self |
BibTeX
@INPROCEEDINGS{Caruana94greedyattribute,
author = {Rich Caruana and Dayne Freitag},
title = {Greedy Attribute Selection},
booktitle = {In Proceedings of the Eleventh International Conference on Machine Learning},
year = {1994},
pages = {28--36},
publisher = {Morgan Kaufmann}
}
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Abstract
Many real-world domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes than if given a good subset of those attributes. We examine this problem for two learning tasks taken from a calendar scheduling domain. We show that ID3/C4.5 generalizes poorly on these tasks if allowed to use all available attributes. We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance. We present a caching scheme that makes attribute hillclimbing more practical computationally. We also compare the results of hillclimbing in attribute space with FOCUS and RELIEF on the two tasks. 1 INTRODUCTION As machine learning is applied to real-world tasks, difficulties arise that do not occur in simpler textbook experiments. One such diffic...
