TY - JOUR 
A1 - Cortizo Pérez, José Carlos
AU - Giráldez Betrón, Juan Ignacio
T1 - Multi criteria wrapper improvements to naive bayes learning
Y1 - 2006
SN - 03029743
UR - http://hdl.handle.net/11268/5493
AB - Feature subset selection using a wrapper means to perform a search for an optimal set of attributes using the Machine Learning Algorithm as a black box. The Naive Bayes Classifier is based on the assumption of independence among the values of the attributes given the class value. Consequently, its effectiveness may decrease when the attributes are interdependent. We present FBL, a wrapper that uses information about dependencies to guide the search for the optimal subset of features and we use the Naive Bayes Classifier as the black-box Machine Learning algorithm. Experimental results show that FBL allows the Naive Bayes Classifier to achieve greater accuracies, and that FBL performs better than other classical filters and wrappers.
KW - Minería de datos
KW - Informática
LA - eng
PB - Springer
ER -