Customer behavior analysis using Naive Bayes with bagging homogeneous feature selection approach

The significant success of an organization greatly depends upon the consumers and their relationship with the organization. The knowledge of consumer behavioral and a excellent understanding of consumer expectations is important for the development of strategic management decisions in support of improving the business value. CRM is intensively applied in the analysis of consumer behavior patterns with the use of Machine Learning (ML) Techniques. Naive Bayes (NB) one of the ML supervised classification models is used to analyze customer behavior predictions. In some domain, the NB performance degrades which involves the existence of redundant, noisy and irrelevant attributes in the dataset, which is a violation of underlying assumption made by naive Bayes. Different enhancements have been suggested to enhance the primary assumption of the NB classifier-independence assumption between the attributes of given class label. In this research, we suggest a simple, straight forward and efficient approach called BHFS (Bagging Homogeneous Feature Selection) which is based upon Ensemble data perturbation feature selection methods. The BHFS method is applied to eliminate the correlated, irrelevant attributes in the dataset and selecting a stable feature subset for improving performance prediction of the NB model. The advantage of the BHFS method requires less running time and selects the best relevant attributes for the evaluation of naive Bayes. The Experimental outcomes demonstrate that the BHFS-naive Bayes model makes better predictions compared to the standard NB. The running time complexity is also less with BHFS-NB since the naive Bayes is constructed using selected features obtained from BHFS.

Automated summary

The success of an organization depends on consumer relationships and understanding consumer behavior is crucial for strategic management decisions. In this research, a feature selection method called BHFS is proposed to improve the performance of the NB model by eliminating irrelevant attributes. BHFS-NB shows better prediction outcomes with less running time complexity compared to standard NB.