Plurality voting-based multiple classifier systems: statistically independent with respect to dependent classifier sets

Demirekler, Mübeccel; Altınçay, Hakan

doi:10.1016/s0031-3203(01)00227-8

Cited by 29 publications

(17 citation statements)

References 17 publications

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“…(24). Since the correct classification probability of each classifier for each class is p, the following constraints for 3 classifiers system immediately follow [17]:…”

Section: Combined Accuracy Using Dependent Classifiersmentioning

confidence: 99%

“…More specifically, the combined accuracy provided by the rule should be analyzed for dependent and independent classifiers cases so as to clarify whether independence is also necessary for achieving good combined accuracies. It is already shown that independent classifiers may perform worse than dependent ones for some combination schemes such as plurality voting [17].…”

Section: Introductionmentioning

confidence: 99%

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On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence

Altınçay

2006

Appl Intell

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In classifier combination, the relative values of beliefs assigned to different hypotheses are more important than accurate estimation of the combined belief function representing the joint observation space. Because of this, the independence requirement in Dempster's rule should be examined from classifier combination point of view. In this study, it is investigated whether there is a set of dependent classifiers which provides a better combined accuracy than independent classifiers when Dempster's rule of combination is used. The analysis carried out for three different representations of statistical evidence has shown that the combination of dependent classifiers using Dempster's rule may provide much better combined accuracies compared to independent classifiers.

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“…(24). Since the correct classification probability of each classifier for each class is p, the following constraints for 3 classifiers system immediately follow [17]:…”

Section: Combined Accuracy Using Dependent Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence

Altınçay

2006

Appl Intell

Self Cite

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“…(6). Since the correct classification probability of each classifier for each class is p, the following constraints for 3 classifiers system immediately follow (Demirekler and Altınçay, 2002):…”

Section: Combined Accuracy Using Dependent Classifiersmentioning

confidence: 99%

“…Moreover, it is already shown that independent classifiers may perform worse than dependent ones for some combination schemes such as plurality voting (Demirekler and Altınçay, 2002). Independence of different features is already studied when naive Bayes approach is used for designing individual classifiers.…”

Section: Introductionmentioning

confidence: 99%

On naive Bayesian fusion of dependent classifiers

Altınçay

2005

Pattern Recognition Letters

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“…Majority and plurality voting are extensively used combination methods (Kittler et al, 1998;Kittler and Alkoot, 2003;Lam and Suen, 1997;Zenobi and Cunningham, 2001;Ruta and Gabrys, 2002;Narasimhamurthy, 2005a;Hansen and Salamon, 1990;Demirekler and Altinc¸ay, 2002). In this paper, the desired relationship is established for the following cases:…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of classifier ensembles in terms of diversity and performance of individual systems

Chung

Hsu

Liu

et al. 2010

International Journal of Pervasive Computing and Communications

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -Multiple classifier systems have been used widely in computing, communications, and informatics. Combining multiple classifier systems (MCS) has been shown to outperform a single classifier system. It has been demonstrated that improvement in ensemble performance depends on either the diversity among or the performance of individual systems. A variety of diversity measures and ensemble methods have been proposed and studied. However, it remains a challenging problem to estimate the ensemble performance in terms of the performance of and the diversity among individual systems. The purpose of this paper is to study the general problem of estimating ensemble performance for various combination methods using the concept of a performance distribution pattern (PDP). Design/methodology/approach -In particular, the paper establishes upper and lower bounds for majority voting ensemble performance with disagreement diversity measure Dis, weighted majority voting performance in terms of weighted average performance and weighted disagreement diversity, and plurality voting ensemble performance with entropy diversity measure D. Findings -Bounds for these three cases are shown to be tight using the PDP for the input set. Originality/value -As a consequence of the authors' previous results on diversity equivalence, the results of majority voting ensemble performance can be extended to several other diversity measures. Moreover, the paper showed in the case of majority voting ensemble performance that when the average of individual systems performance P is big enough, the ensemble performance P m resulting from a maximum (information-theoretic) entropy PDP is an increasing function with respect to the disagreement diversity Dis. Eight experiments using data sets from various application domains are conducted to demonstrate the complexity, richness, and diverseness of the problem in estimating the ensemble performance.

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Plurality voting-based multiple classifier systems: statistically independent with respect to dependent classifier sets

Cited by 29 publications

References 17 publications

On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence

On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence

On naive Bayesian fusion of dependent classifiers

Performance evaluation of classifier ensembles in terms of diversity and performance of individual systems

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