Classification with ensembles and case study on functional magnetic resonance imaging
Digital Communications and Networks(2022)
摘要
The ensemble is a technique that strategically combines basic models to achieve better accuracy rates. Diversity, combination methods, and selection topology are the main factors determining ensemble performance. Consequently, it is a challenging task to design an efficient ensemble scheme. Even though numerous paradigms have been proposed to classify ensemble schemes, there is still much room for improvement. This paper proposes a general framework for creating ensembles in the context of classification. Specifically, the ensemble framework consists of four stages: objectives, data preparing, model training, and model testing. It is comprehensive to design diverse ensembles. The proposed ensemble approach can be used for a wide variety of machine learning tasks. We validate our approach on real-world datasets. The experimental results show the efficiency of the proposed approach.
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关键词
Classification,Ensemble learning,Extreme learning machine
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