Instance-based learning algorithms

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).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1007/BF00153759
Keywords: supervised concept learning, instance-based concept descriptions, incremental learning, learning theory, noise, similarity

ePrint ID: 18494
Date :
Date Event
Date Deposited: 03 Mar 2006
Last Modified: 16 Apr 2017 23:11
Further Information:Google Scholar

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