A FILTER-WRAPPER METHOD TO SELECT VARIABLES FOR THE NAIVE BAYES CLASSIFIER BASED ON CREDAL DECISION TREES
Joaquín Abellán, Andrés R. Masegosa · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2009
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 main class variable. With the Naive Bayes classifier as reference, in this article, our aims are twofold: (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-wrapper selection method using decision trees built with the best possible split criterion obtained in (1).