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• 영문명: Data Reduction for Classification using Entropy-based Partitioning and Center Instances
• 발행기관: 한국산업경영시스템학회
• 저자명: 손승현(Seung-Hyun Son) 김재련(Jae-Yearn Kim)
• 간행물 정보: 『산업경영시스템학회지』제29권 제2호, 13~19쪽, 전체 7쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2006.06.01

영문 초록

  The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

목차

영어 초록
1. 서론
2. 제안 알고리듬
3. 예제 및 분석
4. 실험 결과
5. 결론
참고문헌

키워드

Data mining Data reduction Instance-based learning