A Study on Class-Specifically Discounted Belief for Ensemble Classifiers

Johansson, RonnieHögskolan i Skövde,Institutionen för kommunikation och information,Forskningscentrum för Informationsteknologi,The Distributed Real-Time Systems Research Group,Högskolan i Skövde, Institutionen för kommunikation och information (author)

QC 20180123
Ensemble classifiers are known to generally perform better than their constituent classifiers. Whereas a lot of work has been focusing on the generation of classifiers for ensembles, much less attention has been given to the fusion of individual classifier outputs. One approach to fuse the outputs is to apply Shafer’s theory of evidence, which provides a flexible framework for expressing and fusing beliefs. However, representing and fusing beliefs is non-trivial since it can be performed in a multitude of ways within the evidential framework. In a previous article, we compared different evidential combination rules for ensemble fusion. The study involved a single belief representation which involved discounting (i.e., weighting) the classifier outputs with classifier reliability. The classifier reliability was interpreted as the classifier’s estimated accuracy, i.e., the percentage of correctly classified examples. However, classifiers may have different performance for different classes and in this work we assign the reliability of a classifier output depending on the classspecific reliability of the classifier. Using 27 UCI datasets, we compare the two different ways of expressing beliefs and some evidential combination rules. The result of the study indicates that there is indeed an advantage of utilizing class-specific reliability compared to accuracy in an evidential framework for combining classifiers in the ensemble design considered.

Boström, HenrikHögskolan i Skövde,Institutionen för kommunikation och information,Forskningscentrum för Informationsteknologi,Skövde Cognition and Artificial Intelligence Lab (SCAI),Högskolan i Skövde, Institutionen för kommunikation och information(Swepub:kth)u1r0rr47 (author)
Karlsson, AlexanderHögskolan i Skövde,Institutionen för kommunikation och information,Forskningscentrum för Informationsteknologi,The Distributed Real-Time Systems Research Group,Högskolan i Skövde, Institutionen för kommunikation och information(Swepub:his)karx (author)
Högskolan i SkövdeInstitutionen för kommunikation och information (creator_code:org_t)

In:2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems: IEEE, s. 614-61997814244214429781424421435

By the author/editor: Johansson, Ronni ...; Boström, Henrik; Karlsson, Alexan ...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Information Syst ...

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.