SMS CLASSIFICATION BASED ON NAIVE BAYES CLASSIFIER AND SEMI-SUPERVISED LEARNING

Authors

  • SHEETAL ASHOKRAO SABLE Department of Computer Engineering, SRES College of Engineering, Kopargaon,India
  • PROF. P.N. KALAVADEKAR Department of Computer Engineering, SRES College of Engineering, Kopargaon,India

Keywords:

Short Message Service (SMS, Naïve Baye, Apriori algorithm, spam.

Abstract

Short Message Service is one of the most important media of communication due to the rapid increase of mobile users. A hybrid system of SMS classification is used to detect spam or ham, using various algorithms such as Naïve Bayes classifier and Apriori Algorithm. So there is neededto perform SMS collection, feature selection, pre-processing, vector creation, filtering process and updating system. Two types of SMS classification exists in current mobile phone and they are enlisted as Black and White. Naïve Bayes is considered as one of the most effectual and significant learning algorithms for data mining and machine learning and also has been treated as a core technique in informationretrieval.

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Published

2021-03-27

Issue

Vol. 3 No. 7 (2016): IJIERT

Section

Articles

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How to Cite

SHEETAL ASHOKRAO SABLE, & PROF. P.N. KALAVADEKAR. (2021). SMS CLASSIFICATION BASED ON NAIVE BAYES CLASSIFIER AND SEMI-SUPERVISED LEARNING . International Journal of Innovations in Engineering Research and Technology, 3(7), 1-10. https://repo.ijiert.org/index.php/ijiert/article/view/906