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Feature selection for text classification: A review

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Abstract

Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models.

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Acknowledgments

This work was supported by National Key R&D Plan of China (Grant No. 2017YFB0802203 and 2018YFB100013), National Natural Science Foundation of China (Grant Number U1736203, 61732021, 61472165, 61373158, and 61363009), Guangdong Provincial Engineering Technology Research Center on Network Security Detection and Defense (Grant No. 2014B090904067), Guangdong Provincial Special Funds for Applied Technology Research and Development and Transformation of Important Scientific and Technological Achieve (Grant No. 2016B010124009), the Zhuhai Top Discipline–Information Security, Guangzhou Key Laboratory of Data Security and Privacy Preserving, Guangdong Key Laboratory of Data Security and Privacy Preserving, National Joint Engineering Research Center of Network Security Detection and Protection Technology.

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  1. College of Public Health and Management, Guangxi University of Chinese Medicine, Guangxi, China

    Xuelian Deng & Yuqing Li

  2. College of Information Science and Technology, Jinan University, Guangzhou, China

    Jian Weng

  3. College of Cyber Security, Jinan University, Guangzhou, China

    Jilian Zhang

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Deng, X., Li, Y., Weng, J. et al. Feature selection for text classification: A review. Multimed Tools Appl 78, 3797–3816 (2019). https://doi.org/10.1007/s11042-018-6083-5

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