Generalized class-specific kernelized extreme learning machine for multiclass imbalanced learning

Abstract

Class imbalanced learning is a well-known issue, which exists in real-world applications. Datasets that have skewed class distribution raise hindrance to the traditional learning algorithms. Traditional classifiers give the same importance to all the samples, which leads to the prediction biased towards the majority classes. To solve this intrinsic deficiency, numerous strategies have been proposed such as weighted extreme learning machine (WELM), weighted support vector machine (WSVM), class-specific extreme learning machine (CS-ELM) and class-specific kernelized extreme learning machine (CSKELM). This work focuses on multiclass imbalance problems, which are more difficult compared to the binary class imbalance problems. Kernelized extreme learning machine (KELM) yields better results compared to the traditional extreme learning machine (ELM), which uses random input parameters. This work presents a generalized CSKELM (GCSKELM), the extension of our recently proposed CSKELM, which addresses the multiclass imbalanced problems more effectively. The proposed GCSKELM can be applied directly to solve the multiclass imbalanced problems. GCSKELM with Gaussian kernel function avoids the non-optimal hidden node problem associated with CS-ELM and other existing variants of ELM. The proposed work also has less computational cost in contrast with kernelized WELM (KWELM) for multiclass imbalanced learning. This work employs class-specific regularization parameters, which are determined by employing class proportion. The extensive experimental analysis shows that the proposed work obtains promising generalization performance in contrast with the other state-of-the-art imbalanced learning methods.