Open AccessPerformance Evaluation of Naive Bayes Classifier with and without Filter Based Feature Selection
Author(s) -
Dr.D. Prabha,
Mr. R. Siva Subramanian,
Dr.S. Balakrishnan,
Dr.M. Karpagam
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j9376.0881019
Subject(s) - feature selection , computer science , naive bayes classifier , artificial intelligence , information gain ratio , preprocessor , data mining , data pre processing , machine learning , feature (linguistics) , selection (genetic algorithm) , classifier (uml) , filter (signal processing) , bayes' theorem , pattern recognition (psychology) , bayesian probability , support vector machine , linguistics , philosophy , computer vision
Customer Relationship Ma agement tends to analyze datasets to find insights about data which in turn helps to frame the business strategy for improvement of enterprises. Analyzing data in CRM requires high intensive models. Machine Learning (ML) algorithms help in analyzing such large dimensional datasets. In most real time datasets, the strong independence assumption of Naive Bayes (NB) between the attributes are violated and due to other various drawbacks in datasets like irrelevant data, partially irrelevant data and redundant data, it leads to poor performance of prediction. Feature selection is a preprocessing method applied, to enhance the predication of the NB model. Further, empirical experiments are conducted based on NB with Feature selection and NB without feature selection. In this paper, a empirical study of attribute selection is experimented for five dissimilar filter based feature selection such as Relief-F, Pearson correlation (PCC), Symmetrical Uncertainty (SU), Gain Ratio (GR) and Information Gain (IG).