文章基本信息

标题：Text Classification Based on a Novel Cost-Sensitive Ensemble Multi-Label Learning Method
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作者：Haifeng Hu ; Tao Zhang ; Jiansheng Wu 等
期刊名称：Journal of Software Engineering
印刷版ISSN：1819-4311
电子版ISSN：2152-0941
出版年度：2016
卷号：10
期号：1
页码：42-53
DOI：10.3923/jse.2016.42.53
出版社：Academic Journals Inc., USA
摘要：Text classification is one of the most important tasks in the Natural Language Processing research field. In most cases, text classification is usually a multi-label learning task where, three attributes (i.e., information gain, document frequency and chi-square test values) are widely used to describe documents and the degree of importance of each attribute varies depending on different applications. Hence, it is valuable to improve the prediction performance of text classification by assembling the above attributes. Furthermore, there exists a widespread problem of class imbalance in multi-label learning algorithm. Thus, in this study, a novel cost-sensitive ensemble multi-label learning method CS-EnMLKNN is proposed to assemble the attributes in text classification and deal with the class imbalance problem and a comprehensive framework for solving text classification problems is also proposed accordingly. Finally, experiments on two classic datasets show that our CS-EnMLKNN algorithm outperforms most state-of-the-art multi-label learning algorithms in terms of several learning evaluation criteria.