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Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Shweta Kharya, Sunita Soni

Shweta Kharya and Sunita Soni. Article: Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection. International Journal of Computer Applications 133(9):32-37, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Shweta Kharya and Sunita Soni},
	title = {Article: Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {9},
	pages = {32-37},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


In this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Bayes, and to improve its performance, weighted concept is incorporated. Exploration of Domain knowledge based weight assignment on UCI machine learning repository dataset of breast cancer is performed. As Breast cancer is considered to be second leading cause of death in women today. The experiments show that a weighted naive bayes approach outperforms naive bayes.


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Data Mining, Breast cancer, Naive bayes classifier, Domain based weight, Weights, Posterior probability, UCI machine learning repository, Prediction.