Electrical Engineering and Computer Science Faculty Publications
Document Type
Conference Proceeding
Publication Title
Proceedings of the second international conference on information and knowledge management
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 meta-learning 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 biology sequence 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.
First Page
314
DOI
10.1145/170088.170160
Publication Date
1993
Recommended Citation
Chan, Philip K. and Stolfo, Salvatore J., "Experiments on multistrategy learning by meta-learning" (1993). Electrical Engineering and Computer Science Faculty Publications. 369.
https://repository.fit.edu/ces_faculty/369