Abstract
Social network develops rapidly and thousands of new data appears on the Internet every day. Classification technology is the key to organize big data. Feature Selection (FS) is a direct way to improve classification efficiency. FS can reduce the size of the feature subset and ensure classification accuracy based on features’ score, which is calculated by FS methods. Most previous studies of FS emphasized on precision while time-efficiency was commonly ignored. In our study, we proposed a method named CDFDC at first. It combines both CDF and Category-Frequency. Secondly, we compared DF, CDF, CHI, IG, CDFP_VM and CDFDC to figure out the relationships among algorithm complexity, time efficiency and classification accuracy. The experiment is implemented with 20-news-group data set and NB classifier. The performance of the FS methods evaluated by seven aspects: precision, Micro F1, Macro F1, feature-selection-time, documents-conversion-time, training-time and classification-time. The result shows that the proposed method performs well on efficiency and accuracy when the size of feature subset is greater than 3,000. And it is also discovered that FS algorithm’s complexity is unrelated to accuracy but complexity can ensure time stability and predictability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Basu, T., Murthy, C.A.: Effective text classification by a supervised feature selection approach. In: 12th IEEE International Conference Data Mining Workshops (ICDMW), pp. 918–925. IEEE Press, New York (2012)
Li, Q., He, L., Lin, X.: Improved categorical distribution difference feature selection for Chinese document categorization. In: 8th International Conference on Ubiquitous Information Management and Communication, pp. 102:1–102:7. IEEE Press, New York (2014)
Sharma, A., Dey, S.: A comparative study of feature selection and machine learning techniques for sentiment analysis. In: ACM Research in Applied Computation Symposium, pp. 1–7. IEEE Press, New York (2012)
Wang, Z., Chen, S., Liu, J., Zhang, D.: Pattern representation in feature extraction and classifier design: matrix versus vector. J. IEEE Trans. Neural Netw. 19, 758–769 (2008)
Tariq, A., Karim, A.: Fast supervised feature extraction by term discrimination information pooling. In: 20th ACM International Conference on Information and Knowledge Management, pp. 2233–2236. IEEE Press, New York (2011)
Van, M., Kang, H.-J.: Bearing-fault diagnosis using non-local means algorithm and empirical mode decomposition-based feature extraction and two-stage feature selection. J. IET Sci. Measur. Technol. 9, 671–680 (2015)
Somol, P., Novovicova, J.: Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality. J. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1921–1939 (2010)
Meng, J., Lin, H.: A two-stage feature selection method for text categorization. In: Seventh International Conference Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1492–1496. IEEE Press, New York (2010)
Zhang, W., Yoshida, T., Tang, X.: A comparative study of TF*IDF, LSI and multi-words for text classification. J. Exp. Syst. Appl. 38, 1492–1496 (2011)
Kadhim, A.I., Cheah, Y.N., Ahamed, N.H., Salman, L.A.: Feature extraction for co-occurrence-based cosine similarity score of text documents. In: IEEE Student Conference Research and Development (SCOReD), pp. 1–4. IEEE Press, New York (2014)
Li, Y., Algarni, A., Albathan, M., Shen, Y., Bijaksana, M.A.: Relevance feature discovery for text mining. In: IEEE Transactions on Knowledge and Data Engineering, pp. 1656–1669. IEEE Press, New York (2015)
Li, Y., Algarni, A., Albathan, M., Shen, Y., Bijaksana, M.A.: Relevance feature discovery for text mining. J. IEEE Trans. Knowl. Data Eng. 27, 1656–1669 (2015)
Song, S.J., Heo, G.E., Kim, H.J., Jung, H.J., Kim, Y.H., Song, M.: Grounded feature selection for biomedical relation extraction by the combinative approach. In: ACM 8th International Workshop on Data and Text Mining in Bioinformatics, pp. 29–32. IEEE Press, New York (2014)
Acknowledgments
I feel much indebted to many people who have instructed me in writing this paper. I would like to express my heartfelt gratitude to my tutor, Prof. Wang, for her warm-heart encouragement and most valuable advice, especially for her insightful comments and suggestions on the draft of this paper. Without her help, encouragement and guidance, I could not have completed this paper.
And I would like to express my thanks to my family and my friends for their valuable encouragement and spiritual support during my study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wang, F., Li, X., Huang, X., Kang, L. (2016). Improved Document Feature Selection with Categorical Parameter for Text Classification. In: Boumerdassi, S., Renault, É., Bouzefrane, S. (eds) Mobile, Secure, and Programmable Networking. MSPN 2016. Lecture Notes in Computer Science(), vol 10026. Springer, Cham. https://doi.org/10.1007/978-3-319-50463-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-50463-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50462-9
Online ISBN: 978-3-319-50463-6
eBook Packages: Computer ScienceComputer Science (R0)