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Instance-based learning algorithms (1991)
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| Venue: | Machine Learning |
| Citations: | 1389 - 18 self |
BibTeX
@INPROCEEDINGS{Aha91instance-basedlearning,
author = {David W. Aha and Dennis Kibler and Marc K. Albert},
title = {Instance-based learning algorithms},
booktitle = {Machine Learning},
year = {1991},
pages = {37--66}
}
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Abstract
Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several realworld databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.
Keyphrases
instance-based learning algorithm specific instance performance degrades incremental learning task noise-tolerant decision tree algorithm instance-based learning decision tree storage-reducing algorithm performs classification rule classification prediction noise level training instance several realworld database minor sacrifice neighbor algorithm significance test extended algorithm large storage requirement noisy instance storage requirement classification accuracy attribute noise
