Combining multiple algorithms in classifier ensembles using generalized mixture functions

NEUROCOMPUTING

卷:313

Article

Costa, Valdigleis S.¹  Farias, Antonio Diego S.^1,2  Bedregal, Benjamin¹  Santiago, Regivan H. N.¹  Canuto, Anne Magaly de P.¹ 

[1] Fed Univ Rio Grande Norte UFRN, Dept Informat & Appl Math, BR-59072970 Natal, RN, Brazil

[2] Fed Rural Univ Semiarid UFERSA, Dept Exacts & Nat Sci, BR-59900000 Pau Dos Ferros, RN, Brazil

关键词: Classifier ensembles;    Aggregation functions;    Pre-aggregation functions;    Generalized mixture functions;   

DOI  :  10.1016/j.neucom.2018.06.021

来源: Elsevier

【 摘 要 】

Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate the application of a generalized mixture (GM) functions as a new approach for providing an efficient combination procedure for these systems through the use of dynamic weights in the combination process. Therefore, we present three GM functions to be applied as a combination method. The main advantage of these functions is that they can define dynamic weights at the member outputs, making the combination process more efficient. In order to evaluate the feasibility of the proposed approach, an empirical analysis is conducted, applying classifier ensembles to 25 different classification data sets. In this analysis, we compare the use of the proposed approaches to ensembles using traditional combination methods as well as the state-of-the-art ensemble methods. Our findings indicated gains in terms of performance when comparing the proposed approaches to the traditional ones as well as comparable results with the state-of-the-art methods. (C) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free