Combination of Classifiers With Optimal Weight Based on Evidential Reasoning

Liu, Zhun-ga; Pan, Quan; Dezert, Jean; Martin, Arnaud

doi:10.1109/tfuzz.2017.2718483

Cited by 205 publications

(88 citation statements)

References 41 publications

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“…As already mentioned, different ways of calculating the weights of each class for each individual classifier can be used in a classifier ensemble [10,39,45,46,50,51,57,70]. Although weighted combination methods appear to provide some flexibility, obtaining the optimal weights is not an easy task.…”

Section: Recent Studies In Weighted Combination Methods For In Classimentioning

confidence: 99%

“…One way to improve the efficiency of combination methods is through the use of weights that can be used to denote the confidence (influence) of the individual classifiers in classifying an input pattern to a particular class [47]. Different ways of calculating weights (confidence) of each class for each individual classifier can be used in determining the relative contribution of each classifier within a classifier ensemble and they can be classified as static [39,45,46,50,51,57,70] or dynamic weighting [4,45,51,57]. For offering more flexibility and efficiency, in this paper, we will be working with dynamic weight selection (dynamic weighting).…”

Section: Introductionmentioning

confidence: 99%

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Combining multiple algorithms in classifier ensembles using generalized mixture functions

et al. 2018

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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. This paper is divided into eight sections and it is organized as follows. In Section 2, we describe some recent studies in classifier ensembles. The fundamental notions of the generalized mixture functions are introduced in Section 3, while the basic concepts of classifier ensembles are presented in Section 4. The proposed approach is presented in Section 5. In Section 6, the experimental methodology is presented, while Section 7 presents an analysis of the obtained results of this work. Finally, Section 7 concludes this paper. Recent Studies in Weighted Combination Methods for in Classifier EnsemblesAs already mentioned, different ways of calculating the weights of each class for each individual classifier can be used in a classifier ensemble [10,39,45,46,50,51,57,70]. Although weighted combination methods appear to provide some flexibility, obtaining the optimal weights is not an easy task. Therefore, some optimization techniques have been applied to define the best set of weights, such as in [4,45,51,57]. In [57], for instance, a genetic algorithm (GA) was used to define an optimized set of weights that are used along with the output of the individual classifiers to define the final output of the classifier ensembles. However, all the aforementioned studies apply procedures to define static weights. In other words, these methods define a set of weights that are used throughout the testing phase. This static way to define weights can eventually become inefficient for a classifier ensemble, since the accuracy of an individual classifier can change in the testing search space and this change is not capture by static weights.In a dynamic weighting process, the outputs of all individual classifiers are aggregated and the most competent ones receive the highest weight values. The competence of the classifier o...

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Section: Recent Studies In Weighted Combination Methods For In Classimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining multiple algorithms in classifier ensembles using generalized mixture functions

et al. 2018

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“…Liu, Pan, Dezert, and Martin () examined a combination of classifiers with optimal weight based on evidential reasoning. Evidential reasoning is a tool which provides a framework to represent and combine the uncertain and imprecise information.…”

Section: Weighted Decision Making In Fuzzy Forestsmentioning

confidence: 99%

Fuzzy trees and forests—Review

Sosnowski

Gadomer

2019

WIREs Data Min & Knowl

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Data classification and regression are commonly encountered data analysis problems. Many researchers created multiple tools to deal with these issues. Fuzzy clustering, fuzzy decision trees, and ensemble classifiers such as fuzzy forests are popular tools used for this kind of problems. We would like to describe some interesting, more or less popular, solutions which belong to mentioned areas to show the way they deal with data classification and regression problems. This paper is divided into four parts. In the first part we present the issue of fuzzy clustering, which is one of the most important aspects of fuzzy trees which base on clusters. Some methods of splitting objects into clusters using fuzzy logic are described there. The second part describes different fuzzy decision trees. The way these trees can deal with classification and regression problems is presented. In the third part the issue of forests—ensemble classifiers which consist of fuzzy trees—is described. The last part treats about the way of performing weighted decision making in fuzzy forests. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Classification Technologies > Prediction Technologies > Machine Learning

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“…Multicriteria decision‐making (MCDM) is an important part of modern decision‐making science, which is also widely used in many fields, such as supplier selection, medical diagnosis, sensor fusion, uncertainty modeling, risk analysis, reliability, and so forth . Many effective techniques have been used in solving MCDM problems, such as intuitionistic fuzzy sets, soft sets, evidence theory, evidential reasoning, D number, and Z number . With the development of information science, these problems have attracted many researchers …”

Section: Introductionmentioning

confidence: 99%

An interval‐valued exceedance method in MCDM with uncertain satisfactions

Liu

Xiao

2019

Int J Intell Syst

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Multicriteria decision‐making problems have been applied to many applications for its practicality. Nevertheless, when the evaluated satisfactions are more complex, such as interval‐valued distributions, how to reasonably obtain the aggregation results of alternatives is still an open issue. In this paper, an interval‐valued exceedance method is proposed to solve such a question based on the Golden Rule representative value and probabilistic exceedance method. Due to good performance of expressing uncertain information, the Golden Rule representative value method is used to order interval‐valued satisfactions after an effective normalization process. In addition, a quantifier‐based ordered weighted averaging operator is also introduced to consider the preferences of decision makers. A realistic application of supplier selection is shown to illustrate the practicality of the proposed method.

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Combination of Classifiers With Optimal Weight Based on Evidential Reasoning

Cited by 205 publications

References 41 publications

Combining multiple algorithms in classifier ensembles using generalized mixture functions

Combining multiple algorithms in classifier ensembles using generalized mixture functions

Fuzzy trees and forests—Review

An interval‐valued exceedance method in MCDM with uncertain satisfactions

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