A Diversity-Accuracy Measure for Homogenous Ensemble Selection

A Diversity-Accuracy Measure for Homogenous Ensemble Selection

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...

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Bibliographic Details
Main Authors: Taleb Zouggar, A. Adla
Format: Article
Language:English
Published: Universidad Internacional de La Rioja (UNIR) 2019-06-01
Series:International Journal of Interactive Multimedia and Artificial Intelligence
Subjects:
Bagging
Classification
Decision Trees
Ensemble Methods
Ensemble Pruning
Hill Climbing
Machine Learning
Online Access:http://www.ijimai.org/journal/node/2460

Internet

http://www.ijimai.org/journal/node/2460

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