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IJSTR >> Volume 8 - Issue 11, November 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A New Approach For Naive Bayes For Text Classification With Feature Extraction And Pos Tagging

[Full Text]

 

AUTHOR(S)

Dr.Antony Selvadoss Thanamani, Padmapriya P, Malathi M, Sharmila S, Dr. A. Kanagara

 

KEYWORDS

Naive Bayes classifier, Datasets, Feature extraction, POS Tag

 

ABSTRACT

Text classification is a fundamental development in trademark tongue handling. It might be performed using distinctive classification calculations. It is appeared in ongoing exploration that naive Bays text classifiers have accomplished recognizable classification execution in spite of its solid supposition of contingent freedom among highlights. So as to debilitate this ridiculous supposition and improve the classification precision, there are commonly three techniques: structures controlling, highlights controlling, and occasions controlling. Cases controlling can be additionally isolated into example weighting and case choosing. In this paper, we propose another example weighting way to deal with naive Bayes text classifier. In this new approach, the preparation dataset is initially partitioned into a few subsets as indicated by their promise weight esteem. At that point each preparation occasion in a subset is weighted by the separation among it and the mean of the preparation subset. Thus, it can process complex besides, multi combination data in powerful circumstances. Here we propose an naive bayes classifier which scales straightforwardly with number of markers and information focuses which can be utilized for both double and multiclass classification issues. We actualized the exhibited plans utilizing Java. The trial results exhibit the presentation improvement in the classification strategy utilizing genuine datasets.

 

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