Browsing by Subject "Ensemble size"

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Open Access
Less is more: a comprehensive framework for the number of components of ensemble classifiers
(IEEE, 2019) Bonab, H.; Can, Fazlı
The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of the existing batch and online ensemble classifiers. There are only a limited number of studies on the ensemble size examining majority voting (MV) and weighted MV (WMV). Almost all of them are designed for batch-mode, hardly addressing online environments. Big data dimensions and resource limitations, in terms of time and memory, make the determination of ensemble size crucial, especially for online environments. For the MV aggregation rule, our framework proves that the more strong components we add to the ensemble, the more accurate predictions we can achieve. For the WMV aggregation rule, our framework proves the existence of an ideal number of components, which is equal to the number of class labels, with the premise that components are completely independent of each other and strong enough. While giving the exact definition for a strong and independent classifier in the context of an ensemble is a challenging task, our proposed geometric framework provides a theoretical explanation of diversity and its impact on the accuracy of predictions. We conduct a series of experimental evaluations to show the practical value of our theorems and existing challenges.
Open Access
A theoretical framework on the ideal number of classifiers for online ensembles in data streams
(ACM, 2016-10) Bonab, Hamed R.; Can, Fazlı
A 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.