计算机科学 ›› 2018, Vol. 45 ›› Issue (12): 182-186.doi: 10.11896/j.issn.1002-137X.2018.12.029
彭晓冰1,2, 朱玉全1
PENG Xiao-bing1,2, ZHU Yu-quan1
摘要: 特征加权支持向量机没有考虑特征间的相关性,因此产生的冗余会形成干扰并对最后的分类结果产生负面影响。为解决这个问题,提出了一种基于特征内相关和互信息的特征加权算法,并将其应用于支持向量机。该算法引入了特征间相关系数作为衡量冗余度的一个指标,以此计算出惩罚因子,在特征加权向量机的基础上对权值进行处理,尽可能真实地体现出特征对分类的贡献度。经过多个数据集以及几种不同算法的实验比较,提出的新算法具有更好的鲁棒性和泛化能力。
中图分类号:
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