Abstract
In this work, a text classification method through a filter type feature selection for imbalanced data is addressed. The model initially clusters the documents associated with a class through a hierarchical clustering there by accomplishing a balanced or near balanced class. Later, a filter type feature selection is recommended to choose the most discriminative features for text classification. Subsequently, the documents are stored in the form of interval valued data. For classification purpose, a suitable symbolic classifier is recommended. The experimentation is done with two standard benchmarking datasets viz., Reuters 21578 and TDT2. The experimental results obtained from the proposed model are better in terms of f-measure when compared to the available models.
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Aghdam, M.H., Aghaee, N.G., Basiri, M.E.: Text feature selection using ant colony optimization. Expert Syst. Appl. 36(3)-2, 6843–6853 (2009)
Azam, N., Yao, J.: Comparison of term frequency and document frequency based feature selection metrics in text categorization. Expert Syst. Appl. 39, 4760–4768 (2012)
Elhadad, M.K., Khaled, M., Badran, K.M., Salama, G.: A novel approach for ontology-based dimensionality reduction for web text document classification. In International Conference on Information Systems (ICIS) - 2017, vol. 978, pp. 5090–5507. IEEE (2017)
Guru, D.S., Nagendraswamy, H.S.: Symbolic representation of two-dimensional shapes. Pattern Recognit. Lett. 28, 144–155 (2006)
Guru, D.S., Suhil, M., Guru, D.S., Lavanya, N.R., Vinay Kumar, N.: An alternative framework for univariate filter based feature selection for text categorization. Pattern Recognit. Lett. 103, 23–31 (2018)
Guru, D.S., Suhil, M.: A novel term class relevance measure for text categorization. Procedia Comput. Sci. 45, 13–22 (2015)
Harish, B.S., Guru, D.S., Manjunath, S.: Representation and classification of text documents: a brief review. IJCA Spec. Issue Recent. Trends Image Process. Pattern Recognit. (RTIPPR) 110–119 (2010)
Jiang, S., Pang, S., Wu, M., Kuang, L.: An improved K-nearest-neighbor algorithm for text categorization. Expert Syst. Appl. 39, 1503–1509 (2012)
Junejo, K.A., Karim, A., Tahir, M.H., Jeon, M.: Terms-based discriminative Information space for robust text classification. Inf. Sci. 372, 518–538 (2016)
Raju, L.N., Suhil, M., Guru, D.S., Gowda, H.S.: Cluster based symbolic representation for skewed text categorization. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 202–216. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-4859-3_19
Pinheiro, R.H.W., Cavalcanti, G.D.C., Ren, T.I.: Data-driven global-ranking local feature selection methods for text categorization. Expert Syst. Appl. 42, 1941–1949 (2015)
Pinheiro, R.H.W., Cavalcanti, G.D.C., Correa, R.F., Ren, T.I.: A global-ranking local feature selection method for text categorization. Expert Syst. Appl. 39, 12851–12857 (2012)
Rehman, A., Javed, K., Babri, H.A., Saeed, M.: Relative discrimination criterion - a novel feature ranking method for text data. Expert Syst. Appl. 42, 3670–3681 (2012)
Rehman, A., Javed, K., Babri, H.A.: Feature selection based on a normalized difference measure for text classification. Inf. Process. Manag. 53, 473–489 (2017)
Sabbaha, T., Selamat, A., Selamat, M.H., Fawaz, S., Viedmae, A.E.H., Krejcarg, O.: Modified frequency-based term weighting schemes for text classification. Appl. Soft Comput. 58, 193–206 (2017)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)
Suhil, M., Guru, D.S., Lavanya, N.R., Harsha, S.G.: Simple yet effective classification model for skewed text categorization. In: International Conference on Computing, Communications and Informatics (ICACCI)-2016. IEEE, pp. 904–910 (2016)
Swarnalatha, K., Guru, D.S., Anami, B.S., Suhil, M.: Classwise clustering for classification of imbalanced text data. In: Sridhar, V., Padma, M.C., Rao, K.A.R. (eds.) Emerging Research in Electronics, Computer Science and Technology. LNEE, vol. 545, pp. 83–94. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-5802-9_8. Text categorization. Expert Syst. Appl. 49, 31–47 (2016)
Uysal, A.K.: An improved global feature selection scheme for text classification. Expert Syst. Appl. 43, 82–92 (2016)
Uysal, A.K., Gunal, S.: A novel probabilistic feature selection method for text classification. Knowl.-Based Syst. 36, 226–235 (2012)
Wang, D., Zhang, H., Li, R., Lv, W., Wang, D.: t-Test feature selection approach based on term frequency for text categorization. Pattern Recognit. Lett. 45, 1–10 (2011)
Yang, J., Liu, Y., Zhu, X., Liu, Z., Zhang, X.: A new feature selection based on comprehensive measurement both in inter-category and intra-category for text categorization. Inf. Process. Manag. 48, 741–754 (2012)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of the 14th International Conference on Machine Learning, vol. 97, pp. 412–420 (1997)
Zeina, D.A., Fawaz, S., Anzi, A.: Employing fisher discriminant analysis for Arabic text classification. Comput. Electr. Eng. 000, 1–13 (2017)
Zong, W., Wu, F., Chu, L.K., Sculli, D.: A discriminative and semantic feature selection method for text categorization. Int J. Prod. Econ. 165, 215–222 (2015)
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The author N Vinay Kumar acknowledges the Department of Science and Technology, Govt. of India for their financial support rendered through DST-INSPIRE fellowship.
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Swarnalatha, K., Guru, D.S., Anami, B.S., Kumar, N.V. (2019). A Filter Based Feature Selection for Imbalanced Text Classification. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_18
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DOI: https://doi.org/10.1007/978-981-13-9187-3_18
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