01418nas a2200253   4500000000100000000000100001008004100002260001200043653001900055653002100074653001900095653002100114653001200135653002100147653001800168100001800186700001100204245006700215856009600282300001000378490000600388520075600394022001401150       2019                            d  c06/201910aClassification10aMachine Learning10aDecision Trees10aEnsemble Methods10aBagging10aEnsemble Pruning10aHill Climbing1 aTaleb Zouggar1 aA Adla00aA Diversity-Accuracy Measure for Homogenous Ensemble Selection  uhttps://www.ijimai.org/journal/sites/default/files/files/2018/06/ijimai_5_5_8_pdf_37634.pdf  a63-700 v53 aSeveral selection methods in the literature are essentially based on an evaluation function that determines whether a model M contributes positively to boost the performances of the whole ensemble. In this paper, we propose a method called DIversity and ACcuracy for Ensemble Selection (DIACES) using an evaluation function based on both diversity and accuracy. The method is applied on homogenous ensembles composed of C4.5 decision trees and based on a hill climbing strategy. This allows selecting ensembles with the best compromise between maximum diversity and minimum error rate. Comparative studies show that in most cases the proposed method generates reduced size ensembles with better performances than usual ensemble simplification methods.  a1989-1660