一种基于成对标签的Rakel算法改进

An Improved Rakel Approach Based on Label Pairwise

Rakel(Random k-labelsets)算法从原始标签集中随机选择一部分标签子集,并且使用LP(Label Powerset)算法训练相应的多标签子分类器。由于随机选择标签的原因,导致LP子分类器预测性能不好。本文基于标签的共现关系选择成对标签来训练LP分类器,提出PwRakel(Pairwise Random k-labelsets)算法。该算法通过挖掘标签相关性扩展训练集,有效提高分类性能。实验结果表明,所提出的算法与Rakel算法以及其他算法对比,分类准确度更高。

Rakel（ Random k-labelsets） randomly selects a number of label subsets from the original set of labels and uses the LP（ Label Powerset） method to train the corresponding multi-label classifiers. But the models maybe have a poor performance because of randomization nature. Thus in this paper we firstly capture some pairwise relationships based on label co-occurrence between the labels to training LP classifier by PwRakel（ Pairwise Random k-labelsets） algorithm. The method extends the training set by exploiting label correlations to improve classification performance effectively. The experimental results indicate that the proposed method improves multi-label classification accuracy compared with the Rakel algorithm and to other state-of-the-art algorithms.