Online sequential class-specific extreme learning machine for binary imbalanced learning.

Many real-world applications suffer from the class imbalance problem, in which some classes have significantly fewer examples compared to the other classes. In this paper, we focus on online sequential learning methods, which are considerably more preferable to tackle the large size imbalanced classification problems effectively. For example, weighted online sequential extreme learning machine (WOS-ELM), voting based weighted online sequential extreme learning machine (VWOS-ELM) and weighted online sequential extreme learning machine with kernels (WOS-ELMK), etc. handle the imbalanced learning effectively. One of our recent works class-specific extreme learning machine (CS-ELM) uses class-specific regularization and has been shown to perform better for imbalanced learning. This work proposes a novel online sequential class-specific extreme learning machine (OSCSELM), which is a variant of CS-ELM. OSCSELM supports online learning technique in both chunk-by-chunk and one-by-one learning mode. It targets to handle the class imbalance problem for both small and larger datasets. The proposed work has less computational complexity in contrast with WOS-ELM for imbalanced learning. The proposed method is assessed by utilizing benchmark real-world imbalanced datasets. Experimental results illustrate the effectiveness of the proposed approach as it outperforms the other methods for imbalanced learning.

Class-specific extreme learning machine for handling binary class imbalance problem.

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, ß. 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.