Instance-based learning algorithms

Instance-based learning algorithms (1991)

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by David W. Aha , Dennis Kibler , Marc K. Albert

Venue:	Machine Learning
Citations:	897 - 18 self

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Datum	Value	Source
TITLE	Instance-based learning algorithms	INFERENCE
AUTHOR NAME	David W. Aha	SVM HeaderParse 0.2
AUTHOR NAME	Dennis Kibler	SVM HeaderParse 0.2
AUTHOR NAME	Marc K. Albert	SVM HeaderParse 0.2
AUTHOR AFFIL	; Department of Information and Computer Science, University of California, Irvine; Editor: J.R. Quinlan	SVM HeaderParse 0.2
AUTHOR ADDR	; CA 92717	SVM HeaderParse 0.2
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.	SVM HeaderParse 0.2
YEAR	1991	INFERENCE
VENUE	Machine Learning	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	37--66	INFERENCE
VOLUME	6	INFERENCE
CITATIONS	50 found	ParsCit 1.0