Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Title = "Spam Detection in Social Networks Using Correlation Based Feature Subset Selection",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "4",
Pages ="584 - 632",
Year = "2015",
Authors ="Sanjeev Dhawan