Abstract
This article presents a novel text categorization framework based on Support Vector Machine (SVM) and SMTP similarity mea-sure. The performance of the SVM mainly depends on the selection of kernel function and soft margin parameter C. To reduce the impact of kernel function and parameter C, in this article, a novel text categorization framework called SVM-SMTP framework is developed. In the proposed SVM-SMTP framework, we used Similarity Measure for Text Processing (SMTP) measure in place of optimal separating hyper-plane as categorization decision making function. To assess the efficacy of the SVM-SMTP framework, we carried out experiments on publically available datasets: Reuters-21578 and 20-NewsGroups. We compared the results of SVM-SMTP framework with other four similarity measures viz., Euclidean, Cosine, Correlation and Jaccard. The experimental results show that the SVM-SMTP framework outperforms the other similarity measures in terms of categorization accuracy.
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Revanasiddappa, M.B., Harish, B.S. (2019). A New Framework to Categorize Text Documents Using SMTP Measure. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_48
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