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Comparative Study on Class Imbalance Learning for Credit Scoring

机译:信用评分级别不平衡学习的比较研究

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This paper performs systematic comparative studies on weighted methods including weight C4.5, weighted SVM and weighted rough set with traditional C4.5, SVM and rough set for credit scoring. The experiments show that the weighted methods outperform to the traditional methods when the methods are sensitive to the class distribution.
机译:本文对加权方法进行了系统的对比研究,包括重量C4.5,加权SVM和加权粗糙集,具有传统的C4.5,SVM和粗糙集的信用评分。实验表明,当方法对类分布敏感时,加权方法与传统方法效果。

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