Hybrid of Naive Bayes and Gaussian Naive Bayes for Classification: A Map Reduce Approach
Shikha Agarwal1, Balmukumd Jha2, Tisu Kumar3, Manish Kumar4, Prabhat Ranjan5
1Shikha Agarwal, Department of Computer Science, Central University of South Bihar, Gaya, India.
2Balmukund Jha, Department of Computer Science, Central University of South Bihar, Gaya, India.
3Tishu Kumar, Department of Computer Science, Central University of South Bihar, Gaya, India.
4Manish Kumar, Department of Computer Science, Central University of South Bihar, Gaya, India.
5Prabhat Ranjan, Department of Computer Science, Central University of South Bihar, Gaya, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 14 April 2019 | Manuscript Published on 24 May 2019 | PP: 266-268 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F10540486S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Naive Bayes classifier is well known machine learning algorithm which has shown virtues in many fields. In this work big data analysis platforms like Hadoop distributed computing and map reduce programming is used with Naive Bayes and Gaussian Naive Bayes for classification. Naive Bayes is manily popular for classification of discrete data sets while Gaussian is used to classify data that has continuous attributes. Experimental results show that Hybrid of Naive Bayes and Gaussian Naive Bayes MapReduce model shows the better performance in terms of classification accuracy on adult data set which has many continuous attributes.
Keywords: Naive Bayes, Gaussian Naive Bayes, Map Reduce, Classification.
Scope of the Article: Classification