样本过滤的基因表达数据分类

Gene expression data classification based on sample filtering

针对单个人工神经网络稳定性差、分类精度不高的缺点,提出了基于样本过滤的人工神经网络集成算法,并用于基因表达数据分类.采用基因表达数据集Leukemia进行实验仿真,并与单个BP神经网络、Bagging神经网络集成和支持向量机进行比较.结果表明,样本过滤算法具有更好的稳定性和更高的分类精度.

Introduced a new algorithm in artificial neural network ensemble based on sample filtering that is used to classify gene expression data. Simulations were carried out to verify the proposed strategy using Leukemia data sets, and the test results were compared with those of BP neural network, Bagging neural network ensemble and support vector machine. The results indicate that sample filtering algorithm has better stability and higher classification accuracy.