The process of big data handling refers the efficient management of storage and processing of very large volume of data. The data in a structured and an unstructured format require specific approach for overall handling.The classifiers analyzed in this paper are correlative naïve bayes classifier (CNB), Cuckoo Grey wolf CNB (CGCNB), Fuzzy CNB (FCNB), and Holoentropy CNB (HCNB). These classifiers are based on Bayesian principle and work accordingly. The CNB is developed by extending the standard naïve bayes classifier with applied correlation among the attributes so that it becomes a dependent hypothesis and it is named as a correlative naïve bayes classifier (CNB). The cuckoo search and grey wolf optimization algorithms are integrated with the CNB classifier and significant performance improvement is achieved. The resulting classifier is called as cuckoo grey wolf correlative naïve bayes classifier (CGCNB). The further performance improvements are achieved by incorporating fuzzy theory termed as fuzzy correlative naïve bayes classifier (FCNB) and holoentropy theory termed as Holoentropy correlative naïve bayes classifier (HCNB) respectively. FCNB and HCNB classifiers are comparatively analyzed with CNB and CGCNB and achieved noticeable performance by analyzing with accuracy, sensitivity and specificity analysis.