Coalitional game-based adaboost

Author

Ykhlef, Hadjer ; Bouchaffra, Djamel ; Ykhlef, Faycal

Author_Institution

Dept. of Comput. Sci., Univ. of Blida, Blida, Algeria

fYear

2014

fDate

5-8 Oct. 2014

Firstpage

194

Lastpage

199

Abstract

In this paper, we introduce a modified Adaboost algorithm, named CGAdaboost, based on cooperative game theory. The algorithm iteratively estimates the value or contribution of each weak learner in the classifier ensemble using Shapley value. Experimental results on UCI and Delve Benchmark datasets show that coalitional game based-Adaboost outperforms the original Adaboost by a margin of 2.25%.

Keywords

game theory; learning (artificial intelligence); pattern classification; CGAdaboost; Delve benchmark datasets; Shapley value; UCI; classifier ensemble; coalitional game; cooperative game theory; Accuracy; Algorithm design and analysis; Boosting; Classification algorithms; Game theory; Games; Training; Adaboost; Coalitional Games; Ensemble of Classifiers; Game Theory; Shapley Value;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on

Conference_Location

San Diego, CA

Type

conf

DOI

10.1109/SMC.2014.6973906

Filename

6973906

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=173107