Analysis of Classification Algorithms for using in Vertical Retrieval Systems
Nemanja Popovic, Suzana Stojkovic
International Journal of Computational Linguistics Research, Vol. 12, No. 1, 2021.
DOI: https://doi.org/10.6025/jcl/2021/12/1/1-8
Link: https://www.dline.info/jcl/fulltext/v12n1/jclv12n1_1.pdf
Abstract:
Classification is the most solved and the most used machine learning problem. In a last few decades many classification algorithms have been developed. Because of that, when classification is needed in some problem solving, the best algorithm should always be selected. The problem that is analysed in this paper is choosing classification algorithms that can be used in vertical retrieval system for both document and query classification. We compared SVM, Multinomial
NaÃ¯ve Bayes algorithm, Bernoulli NaÃ¯ve Bayes algorithm and Random forest. The experiments presented in the paper, show that in the long documents classification SVM and Multinomial NaÃ¯ve Bayes algorithms have a similar precision (SVM is a little better), but the Multinomial Naive Bayes algorithm correctly classified 93.14% of queries, while SVM only 22.55%.