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

Son, Seung-Hyun (Industrial Engineering, Hanyang University)
Kim, Jae-Yearn (Industrial Engineering, Hanyang University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.29, no.2, 2006 , pp. 13-19 More about this Journal
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.
Keywords
Data mining; Data reduction; Instance-based learning;
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