结合ReliefF和互信息的多标签特征选择算法

针对传统单标签特征选择算法不能直接应用于多标签数据的问题，提出一种多标签特征选择算法——MML-RF算法.在ReliefF的基础上，MML-RF算法提出新的类内最近邻样本查找方式，并结合多标签的贡献值改进特征权值的计算方法，能很好地适应多标签数据的特点；同时为了减少特征冗余，MML-RF算法以互信息作为特征冗余度量方式，提出一种去冗余方法，能够得到更小的特征子集.实验表明，MML-RF多标签特征选择算法得到的特征子集规模较小，且在多标签数据集上具有很好的分类效果，能够提升多标签学习和数据挖掘工作的效率.

Multi-label Feature Selection Algorithm Based on ReliefF and Mutual Information

In view of the problem that the traditional feature selection algorithm can not be applied to the multi-label learning context, a MML-RF algorithm is presented. The MML-RF improves the way of defining and searching nearest neighbor on the basis of the ReliefF, and introduces a new parameter to consider the contribution values of different labels. The improved weighting formula enables MML-RF to be used to the multi-label dataset. MML-RF algorithm makes use of mutual information as the measure of feature redundancy, and puts forward a solution to redundancy, which can get smaller subset of features. Experiments show that the feature subset of MML-RF is smaller, and has good classification effect on multi-label dataset, which can further enhance the efficiency of subsequent multi-label learning and data mining.