Class-specific extreme learning machine for handling binary class imbalance problem. (September 2018)

• Record Type: Journal Article
• Authors: Raghuwanshi, Bhagat Singh; Shukla, Sanyam
• Is Part Of: Neural networks. Volume 105(2018)
• Page Start: 206
• Page End: 217
• Publication Date: 2018-09
• Subjects: Extreme learning machine -- Class-specific extreme learning machine -- Class imbalance problem -- Classification; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2018.05.011
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Imbalance problem occurs when the majority class instances outnumber the minority class instances. Conventional extreme learning machine (ELM) treats all instances with same importance leading to the prediction accuracy biased towards the majority class. To overcome this inherent drawback, many variants of ELM have been proposed like Weighted ELM, class-specific cost regulation ELM (CCR-ELM) etc. to handle the class imbalance problem effectively. This work proposes class-specific extreme learning machine (CS-ELM), a variant of ELM for handling binary class imbalance problem more effectively. This work differs from weighted ELM as it does not require assigning weights to the training instances. The proposed work also has lower computational complexity compared to weighted ELM. This work uses class-specific regularization parameters. CCR-ELM also uses class-specific regularization parameters. In CCR-ELM the computation of regularization parameters does not consider class distribution and class overlap. This work uses class-specific regularization parameters which are computed using class distribution. This work also differ from CCR-ELM in the computation of the output weight, β . The proposed work has lower computational overhead compared to CCR-ELM. The proposed work is evaluated using benchmark real world imbalanced datasets downloaded from the KEEL dataset repository. The results show that the proposed work has better performance than weighted ELM, CCR-ELM, EFSVM, FSVM, SVM for class imbalance learning.