Optimal reference subset selection for nearest neighbor classification by tabu search
✍ Scribed by Hongbin Zhang; Guangyu Sun
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 145 KB
- Volume
- 35
- Category
- Article
- ISSN
- 0031-3203
No coin nor oath required. For personal study only.
✦ Synopsis
This paper presents an approach to select the optimal reference subset (ORS) for nearest neighbor classiÿer. The optimal reference subset, which has minimum sample size and satisÿes a certain resubstitution error rate threshold, is obtained through a tabu search (TS) algorithm. When the error rate threshold is set to zero, the algorithm obtains a near minimal consistent subset of a given training set. While the threshold is set to a small appropriate value, the obtained reference subset may have reasonably good generalization capacity. A neighborhood exploration method and an aspiration criterion are proposed to improve the e ciency of TS. Experimental results based on a number of typical data sets are presented and analyzed to illustrate the beneÿts of the proposed method. The performances of the result consistent and non-consistent reference subsets are evaluated.