엔트로피 기반 분할과 중심 인스턴스를 이용한 분류기법의 데이터 감소
Data Reduction for Classification using Entropy-based Partitioning and Center Instances
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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.
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- "UCI Repository of Machine Learning Databases Department of Information and ComputerScience. Internet"