arrow
Return

An efficient ensemble pruning approach based on simple coalitional games

delete2017-03-01
delete35
PRE
AI
H
Hadjer Ykhlef *
D
Djamel Bouchaffra
DOI:10.1016/j.inffus.2016.06.003delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
We propose a novel ensemble pruning methodology using non-monotone Simple Coalitional Games, termed SCG-Pruning. Our main contribution is two-fold: (1) Evaluate the diversity contribution of a classifier based on Banzhaf power index. (2) Define the pruned ensemble as the minimal winning coalition made of the members that together exhibit moderate diversity. We also provide a new formulation of Banzhaf power index for the proposed game using weighted voting games. To demonstrate the validity and the effectiveness of the proposed methodology, we performed extensive statistical comparisons with several ensemble pruning techniques based on 58 UCI benchmark datasets. The results indicate that SCG-Pruning outperforms both the original ensemble and some major state-of-the-art selection approaches. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Ensemble pruning
Simple coalitional game
Banzhaf power index
Weighted voting game
Diversity
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Information Fusion cover
Information Fusion
IF:
15.5
Papers:
4.1K
Citations:
2.7W

Organization

U
universite saad dahlab de blida
Scholars:
462
Papers: 365
Citations: 0