基于差异性和准确性的加权调和平均度量的 基因表达数据选择性集成算法

基分类器之间的差异性和单个基分类器自身的准确性是影响集成系统泛化性能的两个重要因素，针对差异性和准确性难以平衡的问题，提出了一种基于差异性和准确性的加权调和平均（D-A-WHA）度量基因表达数据的选择性集成算法。以核超限学习机（KELM）作为基分类器，通过D-A-WHA度量调节基分类器之间的差异性和准确性，最后选择一组准确性较高并且与其他基分类器差异性较大的基分类器组合进行集成。通过在UCI基因数据集上进行仿真实验，实验结果表明，与传统的Bagging、Adaboost等集成算法相比，基于D-A-WHA度量的选择性集成算法分类精度和稳定性都有显著的提高，且能有效应用于癌症基因数据的分类中。

Selective ensemble algorithm for gene expression data based on diversity and accuracy of weighted harmonic average measure

The diversity between base classifiers and the accuracy of single base classifiers itself are two important factors that affect the generalization performance of ensemble system. Aiming at the problem that the diversity and accuracy are difficult to balance, a selective ensemble algorithm for gene expression data based on Diversity and Accuracy of Weighted Harmonic Average (D-A-WHA) was proposed. The Kernel Extreme Learning Machine (KELM) was used as the base classifier, and the diversity and accuracy of base classifiers were adjusted by D-A-WHA measure. Finally, a set of classifiers with high accuracy and high diversity with other base classifiers were selected to ensemble. The experimental results on UCI gene dataset show that compared with traditional Bagging, Adaboost and other ensemble algorithms, the classification accuracy and stability of the selective ensemble algorithm based on D-A-WHA measure are improved significantly,and it can be applied to the classification of cancer gene expression data effectively.