TY - JOUR
KW - Classification
KW - Machine Learning
KW - Decision Trees
KW - Ensemble Methods
KW - Bagging
KW - Ensemble Pruning
KW - Hill Climbing
AU - Taleb Zouggar
AU - A Adla
AB - Several 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.
IS - Regular Issue
M1 - 5
N2 - Several 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.
PY - 2019
SP - 63
EP - 70
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - A Diversity-Accuracy Measure for Homogenous Ensemble Selection
UR - https://www.ijimai.org/journal/sites/default/files/files/2018/06/ijimai_5_5_8_pdf_37634.pdf
VL - 5
SN - 1989-1660
ER -