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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(2): 183-187 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.409

SMS Classification Based on Naïve Bayes Classifier and Apriori Algorithm Frequent Itemset

Ishtiaq Ahmed, Donghai Guan, and Tae Choong Chung
Abstract—In this paper, we propose a hybrid system of SMS classification to detect spam or ham, using Naïve Bayes classifier and Apriori algorithm. Though this technique is fully logic based, its performance will rely on statistical character of the database. Naïve Bayes is considered as one of the most effectual and significant learning algorithms for machine learning and data mining and also has been treated as a core technique in information retrieval. However, by applying user-specified minimum support and minimum confidence, we gain significant improvement on effective accuracy 98.7% from the traditional Naïve Bayes approach 97.4% experimenting on UCI Data Repository.

Index Terms—Short message service (SMS), Naïve Bayes classifier, Apriori algorithm, spam, ham, minimum support, minimum confidence.

Ishtiaq Ahmed is with the Department of Computer Engineering, School of Electronics and Information, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea (e-mail: Ishtiaq.khu@ khu.ac.kr).
Donghai Guan is with the Faculty of Department of Computer Engineering, Kyung Hee University, Republic of Korea (e-mail: donghai@oslab.khu.ac.kr).
Tae Choong Chung is with the Faculty of Department of Computer Engineering, Kyung Hee University, Republic of Korea. He is also with the Artificial Intelligence Lab, Kyung Hee University (e-mail: tcchung@khu.ac.kr).

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Cite: Ishtiaq Ahmed, Donghai Guan, and Tae Choong Chung, "SMS Classification Based on Naïve Bayes Classifier and Apriori Algorithm Frequent Itemset," International Journal of Machine Learning and Computing vol.4, no. 2, pp. 183-187, 2014.

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