Naive Bayes classifier – An ensemble procedure for recall and precision enrichment

Data is essential for an organization to develop and make decisions efficiently and effectively. Machine learning classification algorithms are used to categorize observations into classes. The Naive Bayes (NB) classifier is a classification algorithm based on the Bayes theorem and the assumption that all predictors are independent of one another. Since this algorithm is based on probabilities, it is necessary to explore the sample distribution and feature type. This study presents an NB classifier method with enhanced performance among multidimensional and multivariate datasets, named the Naive Bayes Enrichment Method (NBEM). The NBEM is based on automated feature selection using threshold learning and the division of a dataset into sub-datasets according to the feature type. The main advantage of this method is the use of multiple NB classifiers based on different distributions and their combinations to classify a new observation. The final phase includes a weighted classification function that combines the results into a single output. This method was tested with 20 multivariate datasets and compared to other classification models and NB classifier variations. The results showed up to 76.3% improvement in the recall measure using NBEM and up to 43.9% improvement in the F1 score. Furthermore, we found that the error percentage of our method depended on the number of classes.