Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

中文翻译：

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多标签学习的联合特征选择和分类

多标签学习处理同时具有多个类标签的示例。它已应用于多种应用程序，例如文本分类和图像注释。已经提出了许多用于多标签学习的算法，其中大多数集中在多标签分类问题上，只有少数是特征选择算法。当前的多标签分类模型主要建立在由所有类标签共享的所有功能组成的单个数据表示上。由于每个类别标签可能由其自身的某些特定特征决定，并且分类和特征选择的问题通常是独立解决的，因此，本文提出一种可以对多标签学习执行联合特征选择和分类的新方法，即JFSC。与许多现有方法不同，JFSC通过考虑成对的标签相关性来学习共享功能和特定于标签的功能，并同时在学习到的低维数据表示上构建多标签分类器。与最新技术的比较研究表明，我们提出的方法在多标签学习的分类和特征选择方面均具有竞争优势。