A Heterogeneous AdaBoost Ensemble Based Extreme Learning Machines for Imbalanced Data

Adnan Omer Abuassba, Dezheng Zhang and Xiong Luo
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Adnan Omer Abuassba: University of Science and Technology Beijing (USTB), Beijing, China & Arab Open University - Palestine, Ramallah, Palestine
Dezheng Zhang: University of Science and Technology Beijing (USTB), Beijing, China
Xiong Luo: University of Science and Technology Beijing (USTB), Beijing, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2019, vol. 13, issue 3, 19-35

Abstract: Extreme learning machine (ELM) is an effective learning algorithm for the single hidden layer feed-forward neural network (SLFN). It is diversified in the form of kernels or feature mapping functions, while achieving a good learning performance. It is agile in learning and often has good performance, including kernel ELM and Regularized ELM. Dealing with imbalanced data has been a long-term focus for the learning algorithms to achieve satisfactory analytical results. It is obvious that the unbalanced class distribution imposes very challenging obstacles to implement learning tasks in real-world applications, including online visual tracking and image quality assessment. This article addresses this issue through advanced diverse AdaBoost based ELM ensemble (AELME) for imbalanced binary and multiclass data classification. This article aims to improve classification accuracy of the imbalanced data. In the proposed method, the ensemble is developed while splitting the trained data into corresponding subsets. And different algorithms of enhanced ELM, including regularized ELM and kernel ELM, are used as base learners, so that an active learner is constructed from a group of relatively weak base learners. Furthermore, AELME is implemented by training a randomly selected ELM classifier on a subset, chosen by random re-sampling. Then, the labels of unseen data could be predicted using the weighting approach. AELME is validated through classification on real-world benchmark datasets.

Date: 2019
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