基于标记特征的多标记学习改进算法

基于标记特征的多标记分类算法通过对标记的正反样例集合进行聚类，计算样例与聚类中心间的距离构造样例针对标记的特征子集，并生成新的训练集，在新的训练集上利用传统的二分类器进行分类。算法在构造特征子集的过程中采用等权重方式，忽略了样例之间的相关性。提出了一种改进的多标记分类算法，通过加权方式使生成的特征子集更加准确，有助于提高样例的分类精度。实验表明改进的算法性能优于其他常用的多标记分类算法。

Modified algorithm with label-specific features for multi-label learning

Multi-label learning with label specific features conducts clustering analysis on the label＇ s positive and negative in- stances, and then features being specific to labels are constructed by computing the distance between the instance and the cluster- ing centers.New training sets are generated based on the label-specific features and the classification model is induced by the tra- ditional binary learner.But the feature sets are generated by using the method of equal weight for each instance, it ignores the rel- evance among instances.This paper proposes a modified algorithm to solve the multi-label learning problem. It results in exact feature sets by weighting instances.Experimental results show that the modified algorithm works better than other commonly used multi-label algorithms.