Improving Performance of the k-Nearest Neighbor Classifier by Combining Feature Selection with Feature Weighting.-Reference-Cited by-同舟云学术

Improving Performance of the k-Nearest Neighbor Classifier by Combining Feature Selection with Feature Weighting.

Published:2002 Issue: Volume:17 Page:209-216
ISSN:1346-0714
Container-title:Transactions of the Japanese Society for Artificial Intelligence
language:en
Short-container-title:Transactions of the Japanese Society for Artificial Intelligence

Author:

Bao Yongguang¹,Du Xiaoyong,Ishii Naohiro¹

Affiliation:

1. Department of Intelligence and Computer Science, Nagoya Institute of Technology

Publisher

Japanese Society for Artificial Intelligence

Subject

Artificial Intelligence,Software

Link

http://www.jstage.jst.go.jp/article/tjsai/17/3/17_3_209/_pdf

Reference45 articles.

1. Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms

2. [Aha 94] D.W. Aha & R.L. Bankert: Feature Selection for case-based classification of Cloud Types: An Empirical Comparison, In D.W. Aha (Ed.) Case-Based Reasoning: Papers from the 1994 Workshop ((TR WS-94-10), Menlo Park, CA: AAAI press.

3. [Aha 98] D.W. Aha: Feature weighting for lazy learning algorithms, In H. Liu & H. Motoda (Eds.) Feature Extraction, Construction and Selection: A Data Mining Perspective, Norwell MA: Kluwer, 1998.

4. [Allen 97] L. B. Allen: Selection of Distance Metrics and Feature Subsets for k-Nearest Neighbor Classifiers, PhD thesis, University of Virginia, 1997.

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