A FILTER-WRAPPER METHOD TO SELECT VARIABLES FOR THE NAIVE BAYES CLASSIFIER BASED ON CREDAL DECISION TREES
Variable selection methods play an important role in the field of attribute mining. In the last few years, several feature selection methods have appeared showing that the use of a set of decision trees learnt from a database can be a useful tool for selecting relevant and informative variables regarding a mainclass variable. With the Naive Bayes classifier as reference, in this article, our aims are two fold: (1) to study what split criterion has better performance when a complete decision tree is used to select variables; and (2) to present a filter-wrappers election method using decision trees built with the best possible split criterion obtained in (1).
naive bayes decision tree variable selection classification imprecise probabilities uncertainty measures