Optimization Research and Application of Unbalanced Data Set Multi-classification Algorithm

机译：不平衡数据集多分类算法的优化研究与应用

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This paper studied multi-classification algorithm of unbalanced data. The conventional classifier often directly expands dichotomy algorithm to multi-classification in dealing with unbalanced data multi-classification without paying attention to the relationship among data. According to the effect of data relationship on SVM algorithm improvement, this paper proposed a spatial extension-based SVM algorithm, which can optimize classifier and improve classification accuracy of minority sample data. Finally, the validity of modified algorithm was verified through data set.

机译：本文研究了不平衡数据的多分类算法。在处理不平衡数据的多分类时，传统的分类器常常直接将二分法扩展为多分类，而不关注数据之间的关系。根据数据关系对支持向量机算法改进的影响，提出了一种基于空间扩展的支持向量机算法，该算法可以优化分类器，提高少数样本数据的分类精度。最后，通过数据集验证了改进算法的有效性。

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《International Conference on Intelligent Human-Machine Systems and Cybernetics》|2016年|39-42|共4页
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Leng Ren; Weimin Zhou;
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关键词
Classification algorithms; Support vector machines; Machine learning algorithms; Algorithm design and analysis; Training; Indexes; Data mining;

机译：分类算法;支持向量机;机器学习算法;算法设计与分析;训练;索引;数据挖掘;

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Optimization Research and Application of Unbalanced Data Set Multi-classification Algorithm

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