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Article type: Research Article
Authors: Morring, Brent D. | Martinez, Tony R.
Affiliations: Computer Science Department, Brigham Young University, Provo, UT 84602, USA. E-mail: morringb@axon.cs.byu.edu, martinez@cs.byu.edu
Abstract: Two disadvantages of the standard nearest neighbor algorithm are 1) it must store all the instances of the training set, thus creating a large memory footprint and 2) it must search all the instances of the training set to predict the classification of a new query point, thus it is slow at run time. Much work has been done to remedy these shortcomings. This paper presents a new algorithm WITS (Weighted-Instance Typicality Search) and a modified version, Clustered-WITS (C-WITS), designed to address these issues. Data reduction algorithms address both issues by storing and using only a portion of the available instances. WITS is an incremental data reduction algorithm with O(n2) complexity, where n is the training set size. WITS uses the concept of Typicality in conjunction with Instance-Weighting to produce minimal nearest neighbor solutions. WITS and C-WITS are compared to three other state of the art data reduction algorithms on ten real-world datasets. WITS achieved the highest average accuracy, showed fewer catastrophic failures, and stored an average of 71% fewer instances than DROP-5, the next most competitive algorithm in terms of accuracy and catastrophic failures. The C-WITS algorithm provides a user-defined parameter that gives the user control over the training-time vs. accuracy balance. This modification makes C-WITS more suitable for large problems, the very problems data reductions algorithms are designed for. On two large problems (10,992 and 20,000 instances), C-WITS stores only a small fraction of the instances (0.88% and 1.95% of the training data)while maintaining generalization accuracies comparable to the best accuracies reported for these problems.
Keywords: instance-based learning, nearest-neighbor, instance reduction, pruning, classification
DOI: 10.3233/IDA-2004-8104
Journal: Intelligent Data Analysis, vol. 8, no. 1, pp. 61-78, 2004
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