By Pascal Poncelet; Maguelonne Teisseire; Florent Masseglia

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Quantification methods of classification processes: Concepts of structural α-entropy. Kybernetica, 3, 30-35. Jain, A. , Murty, M. , & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31, 264-323. , & Rousseeuw, P. J. (1990). Finding groups in data—An introduction to cluster analysis. New York: Wiley Interscience. , & John, G. (1997). Wrappers for feature selection. Artificial Intelligence, 273-324. , Wei, J. , Saal, L. , Antonescu, C. , & Meltzer, P. S. (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks.

An important open issue is determining characteristics of data sets that will inform the choice of an optimal value for the β parameter. Also, investigating metric discretization for data with missing values seems to present particular challenges that we intend to consider in our future work. references Barthélemy, J. P. (1978). Remarques sur les propriétés metriques des ensembles ordonnés, Math. sci. , 61, 39-60. Barthélemy, J. , & Leclerc, B. (1995). The median procedure for partitions. In Partitioning data sets, (pp.

The main interest of the proposed approach to attribute selection is the possibility of the supervi- sion of the process allowing the user to opt between quasi-equivalent attributes (that is, attributes that are close relatively to the Barthélemy-Monjardet distance) in order to produce more meaningful classifiers. We compared our approach with two existing attribute set selection techniques: the correlationbased feature (CSF) selection (developed in Hall (1999) and incorporated in the WEKA package) and the wrapper technique, using the “best first” and the greedy method as search methods, and the J48 classifier for the classifier incorporated by the wrapper.

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