Non-parametric Bayesian classification for big data analyzing

Abstract

Big data classification is a basic task in data mining in identifying the class labels for instances based on a set of features. The Naive Bayes classifier is one of the most commonly used methods for classification. Although the strong feature independence assumption in the Naive Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. The correlated Naive Bayes classifier is a generalization of the Naive Bayes classification model considering the dependencies between features. In this paper, we propose a novel non-parametric Bayesian classification model called feature weighted correlative naive Bayes. In the first place, a kernel density method augments the weight of each training instance iteratively based on the estimated posterior probability. Next, we incorporate this probability into the conditional log-likelihood formula, and finally we optimize the weight of each feature value for each class by maximizing the conditional log-likelihood. Experiments have been conducted on the dataset from the National Health and Nutrition Examination Survey and the accuracy of our proposed learner has been compared with the other existing state-of-the-art competitors. The experimental results have demonstrated the effectiveness and efficiency of our proposed learning algorithm.

Keywords

• Big data
• classification
• correlative naive Bayes
• feature weighting
• kernel density
• non-parametric

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