Abstract
In this paper, a new feature selection technique called Term-Class Weight-Inverse-Class Frequency is proposed for the purpose of text classification. The technique is based on selecting the most discriminating features with respect to each class. Nevertheless, the number of selected features by our technique is equal to the multiples of the number of classes present in the collection. The vectors of the document have been built based on varying number of selected features. The effectiveness of the technique has been demonstrated by conducting a series of experiments on two benchmarking text corpora, viz., Reuters-21578 and TDT2 using KNN classifier. In addition, a comparative analysis of the results of the proposed technique with that of the state-of-the-art techniques on the datasets indicates that the proposed technique outperforms several techniques.
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The second author of this paper acknowledges the financial support rendered by the Indian Council for Cultural Relations (ICCR) and the Egyptian Cultural Affairs and Mission Sector.
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Guru, D.S., Ali, M., Suhil, M. (2019). A Novel Feature Selection Technique for Text Classification. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_63
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DOI: https://doi.org/10.1007/978-981-13-1498-8_63
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