| 基于多分类支持向量机的评估模型研究 |
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| 引用本文： | 丛瑞雪.基于多分类支持向量机的评估模型研究[J].数学的实践与认识,2017(1):291-296. |
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| 作者姓名： | 丛瑞雪 |
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| 作者单位： | 哈尔滨金融学院基础教研部,黑龙江哈尔滨,150030 |
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| 基金项目： | 黑龙江省哲学社会科学研究项目扶持共建，基于模糊支持向量机的英语语篇情感分析(13E024) |
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| 摘 要： | 支持向量机中的参数直接影响其推广能力,针对参数选取的主观性,提出基于改进的遗传算法优化其参数,并将其应用于银行个人信用的五等级分类问题中,针对多分类问题,设计了3个二值分类器,不同分类的参数不同,通过实验证实可以达到更精细的分类效果.
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| 关 键 词： | 遗传算法 模糊支持向量机 银行个人信用 |
Scoring Model Based on Support Vector Machine |
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| Abstract: | According to the problem that parameters of SVM (support vector machine) are crucial to the model generalization ability,a method to optimize SVM parameters by using improved genetic algorithm is presented in this paper and used to solve five classifications bank personal credit rating problem.For multi-classification problem,three second-class classifiers with different classification parameters are designed in this paper.The results of experiment show that the method is possible and achieving a better classification results. |
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| Keywords: | genetic algorithm support vector machine personal credit |
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