UnderBagging based reduced Kernelized weighted extreme learning machine for class imbalance learning

Abstract

Extreme learning machine (ELM) is one of the foremost capable, quick genuine esteemed classification algorithm with good generalization performance. Conventional ELM does not take into account the class imbalance problem effectively. Numerous variants of ELM-like weighted ELM (WELM), Boosting WELM (BWELM) etc. have been proposed in order to diminish the performance degradation which happens due to the class imbalance problem. This work proposed a novel Reduced Kernelized WELM (RKWELM) which is a variant of kernelized WELM to handle the class imbalance problem more effectively. The performance of RKWELM varies due to the arbitrary selection of the kernel centroids. To reduce this variation, this work uses ensemble method. The computational complexity of kernelized ELM (KELM) is subject to the number of kernels. KELM generally employ Gaussian kernel function. It employs all of the training instances to act as the centroid. This will lead to computation of the pseudoinverse of N×N matrix. Here, N represents the number of training instances. This operation becomes very slow for the large values of N. Moreover, for the imbalanced classification problems, using all the training instances as the centroid will result in more number of centroids representing the majority class compared to the centroids representing the minority class. This might lead to biased classification model, which favors the majority class instances. So, this work uses a subset of the training instances as the centroid of the kernels. RKWELM arbitrarily chooses Nmin instances from each class which acts as the centroid. The total number of centroids will be N˜=m×Nmin. Here, m represents the number of classes and Nmin is the number of instances belonging to the minority class which has the least number of instances. This reduction in the number of kernels will lead to reduced kernel matrix of size, N˜×N˜ leading to decrease in the computational complexity. This work creates a number of balanced kernel subsets depending on the degree of class imbalance. A number of RKWELM based classification models are produced utilizing these balanced kernel subsets. The ultimate outcome is computed by the majority voting and the soft voting of these classification models. The proposed algorithm is assessed by using the benchmark real-world imbalanced datasets downloaded from the KEEL dataset repository. The experimental results indicate the superiority of the proposed work in contrast with the rest of classifiers for the imbalanced classification problems.