A Diversity-Accuracy Measure for Homogenous Ensemble Selection

Author
Keywords
Abstract
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.
Year of Publication
2019
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
5
Issue
Regular Issue
Number
5
Number of Pages
63-70
Date Published
06/2019
ISSN Number
1989-1660
Citation Key
URL
https://www.ijimai.org/journal/sites/default/files/files/2018/06/ijimai_5_5_8_pdf_37634.pdf
DOI
10.9781/ijimai.2018.06.005
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