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The Power of Decision Tables (1995)
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| Venue: | Proceedings of the European Conference on Machine Learning |
| Citations: | 160 - 5 self |
BibTeX
@INPROCEEDINGS{Kohavi95thepower,
author = {Ron Kohavi},
title = {The Power of Decision Tables},
booktitle = {Proceedings of the European Conference on Machine Learning},
year = {1995},
pages = {174--189},
publisher = {Springer Verlag}
}
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Abstract
. We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing crossvalidation that is applicable to incremental learning algorithms including IDTM. Using incremental cross-validation, it is possible to cross-validate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incre...
