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ISSN : 2005-0461(Print)
ISSN : 2287-7975(Online)
Journal of Society of Korea Industrial and Systems Engineering Vol.29 No.2 pp.13-19
DOI :

손승현, 김재련
한양대학교 산업공학과

# Data Reduction for Classification using Entropy-based Partitioning and Center Instances

Jae-Yearn Kim, Son Seung-Hyun
Industrial Engineering, Hanyang University
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### Abstract

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