计算机科学 ›› 2014, Vol. 41 ›› Issue (12): 211-215.doi: 10.11896/j.issn.1002-137X.2014.12.046
安春霖,陆慧娟,魏莎莎,杨小兵
AN Chun-lin,LU Hui-juan,WEI Sha-sha and YANG Xiao-bing
摘要： 极限学习机的相异性集成算法(Dissimilarity Based Ensemble of Extreme Learning Machine,D-ELM)在基因表达数据分类中能够得到较稳定的分类效果,然而这种分类算法是基于分类精度的,当所给样本的误分类代价不相等时,不能直接实现代价敏感分类过程中的最小平均误分类代价的要求。通过在分类过程中引入概率估计以及误分类代价和拒识代价重新构造分类结果,提出了基于相异性集成极限学习机的代价敏感算法(CS-D-ELM)。该算法被运用到基因表达数据集上,得到了较好的分类效果。
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