Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms

• Published in: The Artificial intelligence review Vol. 11; no. 1-5; pp. 175 - 191
• Main Authors: Zhang, Jianping, Yim, Yee-Sat, Yang, Jumming
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Nature B.V 01.02.1997
• Subjects: Accuracy; Algorithms; Artificial intelligence; Lazy learning; Locally weighted learning; Machine learning; Prediction; Regression analysis; Similarity measures
• ISSN: 0269-2821; 1573-7462
• DOI: 10.1023/A:1006500703083

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.