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Ensemble Learning via Extreme Learning Machines for Imbalanced Data

Ensemble Learning via Extreme Learning Machines for Imbalanced Data

Adnan Omer Abuassba, Dezheng O. Zhang, Xiong Luo
Copyright: © 2020 |Pages: 30
ISBN13: 9781799830382|ISBN10: 1799830381|ISBN13 Softcover: 9781799830399|EISBN13: 9781799830405
DOI: 10.4018/978-1-7998-3038-2.ch004
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MLA

Abuassba, Adnan Omer, et al. "Ensemble Learning via Extreme Learning Machines for Imbalanced Data." Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence, edited by Kwok Tai Chui, et al., IGI Global, 2020, pp. 59-88. https://doi.org/10.4018/978-1-7998-3038-2.ch004

APA

Abuassba, A. O., Zhang, D. O., & Luo, X. (2020). Ensemble Learning via Extreme Learning Machines for Imbalanced Data. In K. Chui, M. Lytras, R. Liu, & M. Zhao (Eds.), Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence (pp. 59-88). IGI Global. https://doi.org/10.4018/978-1-7998-3038-2.ch004

Chicago

Abuassba, Adnan Omer, Dezheng O. Zhang, and Xiong Luo. "Ensemble Learning via Extreme Learning Machines for Imbalanced Data." In Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence, edited by Kwok Tai Chui, et al., 59-88. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-3038-2.ch004

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Abstract

Ensembles are known to reduce the risk of selecting the wrong model by aggregating all candidate models. Ensembles are known to be more accurate than single models. Accuracy has been identified as an important factor in explaining the success of ensembles. Several techniques have been proposed to improve ensemble accuracy. But, until now, no perfect one has been proposed. The focus of this research is on how to create accurate ensemble learning machine (ELM) in the context of classification to deal with supervised data, noisy data, imbalanced data, and semi-supervised data. To deal with mentioned issues, the authors propose a heterogeneous ELM ensemble. The proposed heterogeneous ensemble of ELMs (AELME) for classification has different ELM algorithms, including regularized ELM (RELM) and kernel ELM (KELM). The authors propose new diverse AdaBoost ensemble-based ELM (AELME) for binary and multiclass data classification to deal with the imbalanced data issue.

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