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中文摘要：
为了实现代价敏感分类过程中的最小平均误分类代价的目的，本研究通过在分类过程中引入概率估计以及误分类代价重新构造分类结果，提出了基于极限学习机（extreme learning machine，ELM）的代价敏感算法CS-ELM并在上述算法基础上，引入“拒识代价”，进一步减小了平均误分类代价。算法被运用到基因表达数据集上并与极限学习机、代价敏感决策树、代价敏感BP神经网络和代价敏感支持向量机做对比，可以得出，嵌入拒识的CS-ELM算法能够更好地降低误分类代价，使分类结果更加可靠。
英文摘要：
To get the minimum misclassification of cost-sensitive classification, the algorithm of cost-sensitive extreme learning machine （CS-ELM） was proposed by using probability estimation and misclassification cost to reconstruct the classification results. Then the ＂rejection cost＂ was put into the above algorithm to further reduce the average misclassi- fication cost. This algorithm was applied on the gene expression datasets and compared with extreme learning machine, cost-sensitive decision tree, cost-sensitive BP neural networks and cost-sensitive support vector machine. The experi- ments demonstrated that the CS-ELM embedded rejection cost could reduce the average misclassification cost better and could make the classification result more reliable.
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