Instance-based learning methods like the nearest neighbour classifier have been proven to perform well in pattern classification on many fields. Despite their high classification accuracy, they suffer from high storage requirement, computational cost and sensitivity to noise. In this paper, we present a data reduction method for classification techniques based on entropy-based partitioning and center instances. Experimental results show that the new algorithms achieve a high data reduction rate as well as classification accuracy.

Abstract
 1. 서론
 2. 제안 알고리듬
  2.1 엔트로피 계산
  2.2 거리 계산
  2.3 제안 알고리듬
 3. 실험 결과
 4. 결론
 참고문헌

• 손승현(한양대학교 산업공학과)
• 김재련(한양대학교 산업공학과)