Reduction Techniques for Instance-BasedLearning Algorithms

Authors:
D. Randall Wilson;Tony R. Martinez
Affiliations:
Neural Network & Machine Learning Laboratory, Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. randy@axon.cs.byu.edu;Neural Network & Machine Learning Laboratory, Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. martinez@cs.byu.edu
Venue:
Machine Learning
Year:
2000

Citing 20
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A fast prototype reduction method based on template reduction and visualization-induced self-organizing map for nearest neighbor algorithm

Applied Intelligence
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification

Neurocomputing
Evaluation of hyperbox neural network learning for classification

Neurocomputing
IIvotes ensemble for imbalanced data

Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
Integrating global and local application of random subspace ensemble

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

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Abstract

Instance-based learning algorithms are often faced with the problemof deciding which instances to store for use during generalization.Storing too many instances can result in large memory requirementsand slow execution speed, and can cause an oversensitivity to noise.This paper has two main purposes. First, it provides a survey ofexisting algorithms used to reduce storage requirements ininstance-based learning algorithms and other exemplar-basedalgorithms. Second, it proposes six additional reduction algorithmscalled DROP1–DROP5 and DEL (three of which werefirst described in Wilson & Martinez, 1997c, asRT1–RT3) that can be used to remove instances from the conceptdescription. These algorithms and 10 algorithms from the survey arecompared on 31 classification tasks. Of those algorithms thatprovide substantial storage reduction, the DROP algorithms havethe highest average generalization accuracy in these experiments,especially in the presence of uniform class noise.