Classifying imbalanced data using BalanceCascade-based kernelized extreme learning machine

Abstract

Imbalanced learning is one of the substantial challenging problems in the field of data mining. The datasets that have skewed class distribution pose hindrance to conventional learning methods. Conventional learning methods give the same importance to all the examples. This leads to the prediction inclined in favor of the majority classes. To solve this intrinsic deficiency, numerous strategies have been proposed such as weighted extreme learning machine (WELM) and boosting WELM (BWELM). This work designs a novel BalanceCascade-based kernelized extreme learning machine (BCKELM) to tackle the class imbalance problem more effectively. BalanceCascade includes the merits of random undersampling and the ensemble methods. The proposed method utilizes random undersampling to design balanced training subsets. The proposed ensemble generates the base learner in a sequential manner. In each iteration, the correctly classified examples belonging to the majority class are replaced by the other majority class examples to create a new balanced training subset, i.e., the base learners differ in the choice of the balanced training subset. The cardinality of the balanced training subsets depends on the imbalance ratio. This work utilizes a kernelized extreme learning machine (KELM) as the base learner to build the ensemble as it is stable and has good generalization performance. The time complexity of BCKELM is considerably lower in contrast to BWELM, BalanceCascade, EasyEnsemble and hybrid artificial bee colony WELM. The exhaustive experimental evaluation on real-world benchmark datasets demonstrates the efficacy of the proposed method.