Experiments on Multistrategy Learning by Meta-Learning (1993)
| Venue: | In Proc. Second Intl. Conference on Info. and Knowledge Mgmt |
| Citations: | 70 - 13 self |
BibTeX
@INPROCEEDINGS{Chan93experimentson,
author = {Philip Chan and Salvatore J. Stolfo},
title = {Experiments on Multistrategy Learning by Meta-Learning},
booktitle = {In Proc. Second Intl. Conference on Info. and Knowledge Mgmt},
year = {1993},
pages = {314--323}
}
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Abstract
In this paper, we propose meta-learning as a general technique to combine the results of multiple learning algorithms, each applied to a set of training data. We detail several metalearning strategies for combining independently learned classifiers, each computed by different algorithms, to improve overall prediction accuracy. The overall resulting classifier is composed of the classifiers generated by the different learning algorithms and a meta-classifier generated by a meta-learning strategy. The strategies described here are independent of the learning algorithms used. Preliminary experiments using different strategies and learning algorithms on two molecular biologysequence analysis data sets demonstrate encouraging results. Machine learning techniques are central to automated knowledge discovery systems and hence our approach can enhance the effectiveness of such systems. 1 Introduction The information age provides us with easy access to a wide range of information, however, it ...
