NDLI: A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms

Content Provider	Springer Nature Link
Author	Wettschereck, Dietrich Aha, David W. Mohri, Takao
Copyright Year	1997
Abstract	Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that k-NN's performance is highly sensitive to the definition of its distance function. Many k-NN variants have been proposed to reduce this sensitivity by parameterizing the distance function with feature weights. However, these variants have not been categorized nor empirically compared. This paper reviews a class of weight-setting methods for lazy learning algorithms. We introduce a framework for distinguishing these methods and empirically compare them. We observed four trends from our experiments and conducted further studies to highlight them. Our results suggest that methods which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings. We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others.
Starting Page	273
Ending Page	314
Page Count	42
File Format	PDF
ISSN	02692821
Journal	Artificial Intelligence Review
Volume Number	11
Issue Number	1-5
e-ISSN	15737462
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1997-01-01
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Computer Science Artificial Intelligence (incl. Robotics) Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Linguistics and Language

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