TY - JOUR 
A1 - Cortizo Pérez, José Carlos
AU - Giráldez Betrón, Juan Ignacio
AU - Gaya López, María Cruz
T1 - Wrapping the naive bayes classifier to relax the effect of dependences
Y1 - 2007
UR - http://hdl.handle.net/11268/5437
AB - The Naive Bayes Classifier is based on the (unrealistic) assumption of independence among the values of the attributes given the class value. Consequently, its effectiveness may decrease in the presence of interdependent attributes. In spite of this, in recent years, Naive Bayes classifier is worked for a privilege position due to several reasons. We present DGW (Dependency Guided Wrapper), a wrapper that uses information about dependences to transform the data representation to improve the Naive Bayes classification. This paper presents experiments comparing the performance and execution time of 12 DGW variations against 12 previous approaches, as constructive induction of cartesian product attributes, and wrappers that perform a search for optimal subsets of attributes. Experimental results show that DGW generates a new data representation that allows the Naive Bayes to obtain better accuracy more times than any other wrapper tested. DGW variations also obtain the best possible accuracy more often than the state of the art wrappers while often spending less time in the attribute subset search process.
KW - Minería de datos
KW - Probabilidades
KW - Teoría de las probabilidades
KW - Informática
LA - eng
PB - Springer
ER -