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\iteman{io-port 05344208}
\itemau{Abell\'an, Joaqu{\'\i}n; Masegosa, Andr\'es R.}
\itemti{Split criterions for variable selection using decision trees.}
\itemso{Mellouli, Khaled (ed.), Symbolic and quantitative approaches to reasoning with uncertainty. 9th European conference, ECSQARU 2007, Hammamet, Tunisia, October 31--November 2, 2007. Proceedings. Berlin: Springer (ISBN 978-3-540-75255-4/pbk). Lecture Notes in Computer Science 4724. Lecture Notes in Artificial Intelligence, 489-500 (2007).}
\itemab
Summary: In the field of attribute mining, several feature selection methods have recently appeared indicating that the use of sets of decision trees learnt from a data set can be an useful tool for selecting relevant and informative variables regarding to a main class variable. With this aim, in this study, we claim that the use of a new split criterion to build decision trees outperforms another classic split criterions for variable selection purposes. We present an experimental study on a wide and different set of databases using only one decision tree with each split criterion to select variables for the Naive Bayes classifier.
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\itemcc{}
\itemut{variable selection; classification; decision tree; Naive Bayes; Imprecise probabilities; uncertainty measures}
\itemli{doi:10.1007/978-3-540-75256-1\_44}
\end