Building ensemble classifiers using belief functions and OWA operators

Abstract

A pervasive task in many forms of human activity is classification. Recent interest in the classification process has focused on ensemble classifier systems. These types of systems are based on a paradigm of combining the outputs of a number of individual classifiers. In this paper we propose a new approach for obtaining the final output of ensemble classifiers. The method presented here uses the Dempster-Shafer concept of belief functions to represent the confidence in the outputs of the individual classifiers. The combing of the outputs of the individual classifiers is based on an aggregation process which can be seen as a fusion of the Dempster rule of combination with a generalized form of OWA operator. The use of the OWA operator provides an added degree of flexibility in expressing the way the aggregation of the individual classifiers is performed.

BeeOWA: A novel approach based on ABC algorithm and induced OWA operators for constructing one-class classifier ensembles

“…On the other hand, if continuous outputs like posterior probabilities are given, the mean or some other linear fusion rules are applied [21,36]. Also, if the outputs are interpreted as fuzzy membership values, fuzzy rules [37,38] and belief functions [39] are used.…”

“…Due to their simplicity and robustness, they have been widely used in many applications, such as database systems [46], fuzzy logic controllers [47], classifier ensembles [39], and so on. In the following, we give a brief introduction to the OWA operators.…”

A study on class-specifically discounted belief for ensemble classifiers

“…The construction of ensemble classifiers can generally be divided into two parts: generation of classifiers and combination method design [11,Sec. 2].…”

Ordered visibility graph weighted averaging aggregation operator: A methodology based on network analysis

“…To address decision making issues under uncertain environment, fuzzy sets theory (Jiang, Luo, Qin & Zhan, 2015a;Jiang, Yang, Luo & Qin, 2015b;Kahraman, Onar & Oztaysi, 2015;Liu, Liu & Lin, 2013a;Liu, 2014;Rikhtegar et al, 2014;Zadeh, 1965) and evidence theory (Bandyopadhyay & Bhattacharya, 2015;Dempster, 1967;Deng, Mahadevan & Zhou, 2015;Deng, 2015;Fu & Yang, 2012;Kabir, Tesfamariam, Francisque & Sadiq, 2015;Liang, Pedrycz, Liu & Hu, 2015;Liu, Pan & Dezert, 2013b;Liu, Pan, Dezert & Mercier, 2014;Shafer, 1976;Su, Mahadevan, Xu & Deng, 2015;Wang, Dai, Chen & Meng, 2015) are widely used to model uncertain information. As a result, many OWA operators combined with fuzzy set theory and evidence theory are presented Yang and Pang (2014) and Reformat and Yager (2007). Generally speaking, OWA will play a more and more important role in decision making field.…”