Abstract: Random forest has been proved to be an efficient algorithm for classification and feature selection in bioinformatics.Although the effect of parameter setting on results is very limited，a group of appropriate parameters can generate excellent performance.This paper focuses on the setting of class weights in random forest to deal with classification and feature selection problems of unbalanced small-sample data and determines the optimal class weight.In order to compare the performance of feature selection with different weights，SVM is applied in the paper.The results show that optimal class weight is variable and cannot form a standard.However，people can find some weights with which not only classification but also feature selection can get better performance.

Key words: random forest, class weight, small-sample, Support Vector Machine（SVM）, feature selection

摘要： 随机森林已经被证明是一种高效的分类与特征选择方法。尽管参数的设置对结果影响较小，但合适的参数可以使分类器得到理想的效果。主要针对癌症研究中小样本不均衡数据的分类和特征选择问题，研究了随机森林中类权重的设置。为了比较在不同的类权重下特征选择的效果，同时使用支持向量机（Support Vector Machine，SVM）方法。最终结果显示最优的类权重是不确定的。最后总结出几条规律指导研究者选择合适的权重使分类和特征选择效果得到改善。

关键词: 随机森林, 类权重, 小样本, 支持向量机, 特征选择