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《Journal of Jinan University(Natural Science & Medicine Edition)》 2009-01
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A rare-class classification approach based on Clustering and Ripper

YU Wen1,JIANG Sheng-yi1,HUANG Xing-quan2(1.College of Information,Guangdong University of Foreign Studies,Guangzhou 510006,China;2.Guangdong Lancoo Co,Limited,Guangzhou 510540,China)  
The rare-class classification is an important issue in many real life applications;this paper considers the rare-class datasets are easily ignored in the classification because of its low proportion of the whole datasets.We apply a rare-class classification approach based on clustering and Ripper.This approach is trying to find out the rare-class datasets after Cluster through recognizing every cluster whose proportion of the whole datasets is lower than 15% as the rare-class datasets.After that,Ripper algorithm is used to classify both the rare-class datasets and the normal-class datasets separately.The rule set of the whole datasets will be created by the certain method of this approach according to the model which has already been set up above.The experiments carried on benchmark datasets from the UCI Machine Learning Repository show that this approach creates high quality classifying.This approach can also be implemented to classify the rare-class datasets in some practical life applications.
【Fund】: 国家自然科学基金项目(60673191);; 广东省高等学校自然科学研究重点项目(06Z012);; 广东外语外贸大学科研创新团队项目(GW2006-TA-005)
【CateGory Index】: TP18
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【Co-citations】
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