基于支持向量机的不均衡文本分类方法Unbalanced text classification method based on support vector machine
高超,许翰林
摘要(Abstract)：
目前支持向量机(SVM)对均衡文本数据集进行文本分类时表现十分良好,但如果文本数据集是不均衡的,尤其是当不均衡率很大时,容易导致支持向量机分类失败。提出PSO-SMOTE混合算法,针对不均衡文本数据集问题,运用SMOTE算法生成插值样本均衡数据集,并通过PSO算法迭代进化得到最佳的插值样本,对支持向量机的文本分类能力进行优化。实验结果表明,新算法大幅优化了支持向量机分类不均衡文本数据集的能力。
关键词(KeyWords)： 混合算法;支持向量机;不均衡数据集;插值样本;文本分类;迭代进化
基金项目(Foundation):
作者(Author): 高超,许翰林
DOI: 10.16652/j.issn.1004-373x.2018.15.041
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