计算机科学 ›› 2014, Vol. 41 ›› Issue (9): 243-247.doi: 10.11896/j.issn.1002-137X.2014.09.046
魏莎莎,陆慧娟,安春霖,郑恩辉,金伟
WEI Sha-sha,LU Hui-juan,AN Chun-Lin,ZHENG En-hui and JIN Wei
摘要: 针对大规模基因芯片高维度的基因表达数据存在大量无关和冗余特征可能降低分类器性能的问题,提出了一种基于互信息最大化方法(MMI)和与遗传算法的模型无关的基因选择方法来将特征选择转化为全局优化问题,其中的适应度函数定义为类间距离与类内距离之比,适应程度高。为了评价算法的性能,采用3个数据集进行了实验,结果表明MMIGA-Selection取得了较好的效果,在每个数据集上获得了较高的5折交叉验证正确率。MMIGA-Selection主要有两个优点:一是可以有效减少冗余基因;二是模型无关性,选择得出的特征子集可直接用于其他类型的分类器,分类精度较高。
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