Title: Multiple Correlation Encoder-based Naive Bayes
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Naive Bayes, attribute conditional independence, multiple correlation encoder

Abstract: Naive Bayes (NB) continues to be one of the top 10 data mining algorithms. However, due to its assumption of attribute conditional independence, NB encounters significant challenges in addressing attribute-class correlations, attribute-attribute correlations, instance-class correlations, instance-instance correlations, and so on. In the last few decades, a large number of improved algorithms have been proposed, but none of them simultaneously addresses all these correlations. To bridge this gap, this paper proposes a novel algorithm called multiple correlation encoder-based naive Bayes (MCENB). In MCENB, we first design a multiple correlation encoder to generate new attributes, where multiple correlations are simultaneously captured and optimized. Specifically, the newly generated attributes are highly correlated with the class, yet uncorrelated with each other. Instances consisting of new attribute values are highly correlated with those in the same class. Subsequently, we augment original attributes by concatenating them with new attributes. Finally, we weight each augmented attribute to alleviate the attribute redundancy and then build NB on the weighted attributes. The experiments across numerous datasets show that MCENB significantly outperforms its benchmark competitors.

TL;DR: Multiple Correlation Encoder-based Naive Bayes