Bonab, Hamed R.Can, Fazlı2018-04-122018-04-122016-10http://hdl.handle.net/11693/37519Date of Conference: 24-28 October, 2016Conference name: CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge ManagementA priori determining the ideal number of component classifiers of an ensemble is an important problem. The volume and velocity of big data streams make this even more crucial in terms of prediction accuracies and resource requirements. There is a limited number of studies addressing this problem for batch mode and none for online environments. Our theoretical framework shows that using the same number of independent component classifiers as class labels gives the highest accuracy. We prove the existence of an ideal number of classifiers for an ensemble, using the weighted majority voting aggregation rule. In our experiments, we use two state-of-the-art online ensemble classifiers with six synthetic and six real-world data streams. The violation of providing independent component classifiers for our theoretical framework makes determining the exact ideal number of classifiers nearly impossible. We suggest upper bounds for the number of classifiers that gives the highest accuracy. An important implication of our study is that comparing online ensemble classifiers should be done based on these ideal values, since comparing based on a fixed number of classifiers can be misleading. © 2016 ACM.EnglishBig data streamEnsemble sizeWeighted majority votingData communication systemsKnowledge managementData streamEnsemble classifiersIndependent componentsNumber of componentsResource requirementsTheoretical frameworkWeighted majority votingBig dataConference Paper10.1145/2983323.2983907