At present, the common operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers pointed out the potentialities of “dynamic classifier selection” as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative arguments. This paper is aimed to provide a theoretica...

We introduce the ‘No Panacea Theorem’ for classifier combination in the two-classifier, two-class case. It states that if the combination function is continuous and diverse, there exists a situation in which the combination algorithm will always give very bad performance. Thus, there is no optimal algorithm, suitable in all situations. From this theorem, we see that the probability density func...

An approach to classifier combination based on the concept of “dynamic classifier selection” is presented in this letter. The results are reported and they show that the proposed approach allows to develop effective image classification systems.

An approach to classifier combination based on the concept of “dynamic classifier selection” is presented in this letter. The results are reported and they show that the proposed approach allows to develop effective image classification systems.

The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to...

In pattern recognition, there is a growing use of multiple classifier combinations with the goal to increase recognition performance. In many cases, plurality voting is a part of the combination process. In this article, we discuss and test several well known voting methods from politics and economics on classifier combination in order to see if an alternative to the simple plurality vote exist...

Citizen science and human computation involves working with multiple, untrusted decision makers. We demonstrate how Bayesian Classifier Combination outperforms a naive Bayes method when classifying documents using unreliable crowdsourced labels. We also present methods for screening workers and selecting informative documents to label. Finally, we explain how the Bayesian Classifier Combination...

Citizen science and human computation involves working with multiple, untrusted decision makers, whose performance depends on training, rewards, ability and interest. We first present methods for screening workers and selecting informative objects to label. We then demonstrate Bayesian Classifier Combination as an effective method for classifying documents using unreliable crowdsourced labels. ...