基于预分类的FSVM

计算机科学 ›› 2013, Vol. 40 ›› Issue (9): 230-233.

基于预分类的FSVM

申丰山

郑州大学信息工程学院郑州450001

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金(61070137,9)资助

FSVM Based on Pre-classification

SHEN Feng-shan

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 模糊支持向量机(fuzzy support vector machine,FSVM)通过为每个样例设置模糊化训练参数,达到抑制离群点及噪声数据对分类器不利影响的目的。提出了基于预分类的FSVM,每个样例的模糊权重通过关联于该样例的预分类面来确定。该方法不仅考虑了各个样例在未来分类中的作用效果,还考虑了分类器对离群点及噪声数据的敏感性。这样确定的模糊权重能使SVM根据离群点及噪声数据的影响情况决定抑制强度,减少或避免无视数据具体特征的盲目抑制。在IDA、UCI等标准数据集上的实验验证了所提方法的合理性和有效性。

关键词: 模糊支持向量机(FSVM),预分类面,模糊权重,敏感性中图法分类号TP391.4文献标识码A

Abstract: A fuzzy support vector machine(FSVM)reduces the bad effect of outliers or noises on the classifier by using different training parameters for different training examples．This paper proposed an FSVM based on pre-classification,in which fuzzy weight for each training example is computed according to a classification hyperplane created for the training example．The method considers not only the effect of each individual training example,but also the sensitivity of the classifier to the outliers or noises．SVM with such fuzzy weights is able to make proper suppression for the training data．Experimental results on the standard training datasets from IDA and UCI repositories show the reasonableness and effectiveness of the proposed algorithm.

Key words: Fuzzy support vector machine(FSVM),Pre-classification hyperplane,Fuzzy weight,Sensitivity

申丰山. 基于预分类的FSVM[J]. 计算机科学, 2013, 40(9): 230-233. https://doi.org/

SHEN Feng-shan. FSVM Based on Pre-classification[J]. Computer Science, 2013, 40(9): 230-233. https://doi.org/

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