Abstract
Lazy learning methods for function prediction use different prediction functions. Given a set of stored instances, a similarity measure, and a novel instance, a prediction function determines the value of the novel instance. A prediction function consists of three components: a positive integer k specifying the number of instances to be selected, a method for selecting the k instances, and a method for calculating the value of the novel instance given the k selected instances. This paper introduces a novel method called k surrounding neighbor (k-SN) for intelligently selecting instances and describes a simple k-SN algorithm. Unlike k nearest neighbor (k-NN), k-SN selects k instances that surround the novel instance. We empirically compared k-SN with k-NN using the linearly weighted average and local weighted regression methods. The experimental results show that k-SN outperforms k-NN with linearly weighted average and performs slightly better than k-NN with local weighted regression for the selected datasets.
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© 1997 Springer Science+Business Media Dordrecht
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Zhang, J., Yim, YS., Yang, J. (1997). Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms. In: Aha, D.W. (eds) Lazy Learning. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-2053-3_7
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DOI: https://doi.org/10.1007/978-94-017-2053-3_7
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4860-8
Online ISBN: 978-94-017-2053-3
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