JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Nearest Neighbor Based Prototype Classification Preserving Class Regions |
| 영문제목(English Title) |
Nearest Neighbor Based Prototype Classification Preserving Class Regions |
| 저자(Author) |
Doosung Hwang
Daewon Kim
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| 원문수록처(Citation) |
VOL 13 NO. 05 PP. 1345 ~ 1357 (2017. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
A prototype selection method chooses a small set of training points from a whole set of class data. As the data size increases, the selected prototypes play a significant role in covering class regions and learning a discriminate rule. This paper discusses the methods for selecting prototypes in a classification framework. We formulate a prototype selection problem into a set covering optimization problem in which the sets are composed with distance metric and predefined classes. The formulation of our problem makes us draw attention only to prototypes per class, not considering the other class points. A training point becomes a prototype by checking the number of neighbors and whether it is preselected. In this setting, we propose a greedy algorithm which chooses the most relevant points for preserving the class dominant regions. The proposed method is simple to implement, does not have parameters to adapt, and achieves better or comparable results on both artificial and real-world problems. |
| 키워드(Keyword) |
Class Prototype
Dissimilarity
Greedy Method
Nearest-Neighbor Rule
Set Cover Optimization
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| 파일첨부 |
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