DocumentCode :
2979482
Title :
GA-based approaches for finding the minimum reference set for nearest neighbor classification
Author :
Nakashima, Tomoharu ; Ishibuchi, Hisao
Author_Institution :
Dept. of Ind. Eng., Osaka Prefecture Univ., Japan
fYear :
1998
fDate :
9-9 May 1998
Firstpage :
709
Lastpage :
714
Abstract :
In this paper, we examine the ability of genetic algorithms to find a compact reference set for nearest neighbor classification. The task of genetic algorithms is to select a small number of reference patterns from a large number of given training patterns. Our pattern selection problem has two objectives: to maximize the classification performance of the reference set and to minimize the size of the reference set. In our genetic algorithm, they are combined into a single scalar fitness function using a constant weight for each objective. Thus our pattern selection problem is handled as a single-objective combinatorial optimization problem with 0-1 variables where "1" means the inclusion of the corresponding pattern in the reference set and "0" means the exclusion. In this paper, we first briefly explain our genetic algorithm for the pattern selection problem for nearest neighbor classification. Next we examine the ability of the genetic algorithm to find a compact reference set by computer simulations on commonly used real-world pattern classification problems. Finally, we suggest some extensions of our genetic algorithm.
Keywords :
combinatorial mathematics; genetic algorithms; pattern classification; compact reference set; genetic algorithm; genetic algorithms; minimum reference set; nearest neighbor classification; pattern selection problem; real-world pattern classification problems; reference patterns; scalar fitness function; single-objective combinatorial optimization problem; training patterns; Computational modeling; Computer simulation; Databases; Genetic algorithms; Genetic mutations; Industrial engineering; Iris; Nearest neighbor searches; Pattern classification; Size measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
Conference_Location :
Anchorage, AK, USA
Print_ISBN :
0-7803-4869-9
Type :
conf
DOI :
10.1109/ICEC.1998.700139
Filename :
700139
Link To Document :
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