Prototype selection based on minimal consistent subset and genetic algorithms

Abstract

This paper applies the genetic algorithms to identify the minimal "consistent" prototype subset [1]. This subset can be used as a prototype which correctly recognizes the entire original prototype set. This proposed genetic algorithm tries to And the minimal consistent subset to reduce recognition time in nearest neighbor [2] classification. The main difference from other genetic algorithm (GA) approaches is the hybrid of minimal consistent set identification (MCSI) method [3] and genetic algorithm. The MCSI method provides the local optimal number of prototype while the Genetic performs the global search. The proposed hybrid algorithm has been tested on several problems and compared with the results of MCSI and other GA approach [4]. © 2008 SICE.