Bagging of Credal Decision Trees for Imprecise Classification
Metadatos
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Moral García, Serafín; Mantas Ruiz, Carlos Javier; García Castellano, Francisco Javier; Benítez Estévez, María Dolores; Abellán Mulero, JoaquínEditorial
Elsevier
Materia
Imprecise classification Credal decision trees Ensembles Bagging Combination technique
Fecha
2020-03-01Referencia bibliográfica
Moral-García, S., Mantas, C. J., Castellano, J. G., Benítez, M. D., & Abellán, J. (2020). Bagging of Credal Decision Trees for Imprecise Classification. Expert Systems with Applications, 141(1). Doi: 10.1016/j.eswa.2020.112944
Patrocinador
This work has been supported by the Spanish “Ministerio de Economía y Competitividad” and by “Fondo Europeo de Desarrollo Regional” (FEDER) under Project TEC2015-69496-R.Resumen
The Credal Decision Trees (CDT) have been adapted for Imprecise Classification (ICDT). However, no ensembles of imprecise classifiers have been proposed so far. The reason might be that it is not a trivial question to combine the predictions made by multiple imprecise classifier. In fact, if the combination method used is not appropriate, the ensemble method could even worse the performance of one single classifier. On the other hand, the Bagging scheme has shown to provide satisfactory results in precise classification, specially when it is used with CDTs, which are known to be very weak and unstable classifiers. For these reasons, in this research, it is proposed a new Bagging scheme with ICDTs. It is presented a new technique for combining predictions made by imprecise classifiers that tries to maximize the precision of the bagging classifier. If the procedure for such a combination is too conservative it is easy to obtain few information and worse the results of a single classifier. Our proposal considers only the states with the minimum level of non-dominance. An exhaustive experimentation carried out in this work has shown that the Bagging of ICDTs, with our proposed combination technique, performs clearly better than a single ICDT.