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A new classifier ensemble methodology based on subspace learning

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Different classifiers with different characteristics and methodologies can complement each other and cover their internal weaknesses; so classifier ensemble is an important approach to handle the weakness of single classifier based systems. In this article we explore an automatic and fast function to approximate the accuracy of a given classifier on a typical dataset. Then employing the function, we can convert the ensemble learning to an optimisation problem. So, in this article, the target is to achieve a model to approximate the performance of a predetermined classifier over each arbitrary dataset. According to this model, an optimisation problem is designed and a genetic algorithm is employed as an optimiser to explore the best classifier set in each subspace. The proposed ensemble methodology is called classifier ensemble based on subspace learning (CEBSL). CEBSL is examined on some datasets and it shows considerable improvements.
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Keywords: classifier ensemble; classifier selection; genetic algorithms; subspace learning

Document Type: Research Article

Affiliations: 1: Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran 2: School of Electrical Engineering and Computer Science, Faculty of Engineering and Built Environment, Department of Computer Science, University of Newcastle, Callaghan, NSW, Australia 3: School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

Publication date: June 1, 2013

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