Exponential Loss Margin Based Multi-label Feature Selection And Its Application

In multi-label learning tasks,each sample can be associated with multiple labels.There is a wider application space than the single-label learning problem.Multi-label learning problem employs correlation information to improve the performance of the algorithm.Multi-label learning in terms of dimensions and redundancy,need to design a corresponding multi-label algorithm.Multi-label learning problem employs correlation information to improve the performance of the algorithm.In the multi-labeling process,the traditional feature selection algorithm is no longer applicable and generally designed to criteria for single markers.In the multi-label learning,it is necessary to optimize multiple tags at the same time.And there is a certain amount of associated information between different tags in multi-label learning.Therefore,it is necessary to design a feature selection algorithm capable of handling multi-labeling problems,and the algorithm is capable of extracting and utilizing association information between labels.In this article,an improved multi-label feature selection algorithm based on exponential loss margin is proposed,and a method for measuring the similarity of multi-label data samples,benefiting from the large margin based multi-label feature selection algorithm.The algorithm combines the information of feature space with mark space according to sample similarity.The correlation information is also independent of the specific classification algorithm or transformation strategy.The experiments on the datasets demonstrate the correctness and high performance of the proposed algorithm.