Based on miRNAs expression profiling data sets , new data mining algorithms— tSVM‐kNN (t statistic with support vector machine‐k nearest neighbor ) is proposed . Firstly , an original selection is made to this set by characteristics using t‐statistic method . After that , both ideas in support vector machine(SVM )and k nearest neighbor (kNN )algorithms are combined as a classifier , i.e. , SVM‐kNN algorithm .Finally ,the classification results as outputs can be obtained .Then ,simulation experiments show that SVM‐kNN algorithm as a classifier can display a stronger ability compared with running SVM and kNN , respectively . As to the aspects of quantity and recognition accuracy with a miRNAs label , tSVM‐kNN algorithm only need five miRNAs but can get a precision of 96.08% in classification . Obviously , compared with some existed methods , the proposed algorithm has more advantages .%基于 miRNA 表达谱数据集，提出了一种新的数据挖掘算法——— tSVM‐kNN （t statistic with support vector machine‐k nearest neighbor）．该算法的思想为：首先，采用统计量法对该数据集进行特征初选；其次，将融合了支持向量机和K‐最近邻判别法思想的算法———SVM‐kNN算法作为分类器；最后，输出分类结果．仿真实验表明， SVM‐kNN算法分类器的分类能力比单独运行SVM和 kNN都好；在miRNA “标签”的数量和识别精度方面， tSVM‐kNN算法只需要取5个miRNAs即可获得96.08％的分类准确率．与同类的算法相比，其具有明显的优越性．
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