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Instance-based learning algorithms

Record type: Article

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 real-world 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.

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Citation

Aha, David W., Kibler, Dennis and Albert, Marc K. (1991) Instance-based learning algorithms Machine Learning, 6, (1), pp. 37-66. (doi:10.1007/BF00153759).

More information

Published date: 1991
Keywords: supervised concept learning, instance-based concept descriptions, incremental learning, learning theory, noise, similarity

Identifiers

Local EPrints ID: 18494
URI: http://eprints.soton.ac.uk/id/eprint/18494
PURE UUID: 769d4710-de9d-4f43-8818-4a47716b5bd4

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Date deposited: 03 Mar 2006
Last modified: 17 Jul 2017 16:35

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Contributors

Author: David W. Aha
Author: Dennis Kibler
Author: Marc K. Albert

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