Genetic algorithm based weighted extreme learning machine for binary imbalance learning

Class imbalance problem refers to unequal distribution of data instances between classes. Due to this, popular classifiers misclassify data instances of minority class into majority class. Initially, Extreme learning machine was proposed with the prime objective of handling real valued datasets. Though, it a fast learning technique, it suffers from the drawback of misclassification of imbalanced dataset which leads to the class imbalance problem. So, a new variant of ELM called Weighted Extreme Learning Machine was developed. This technique aimed at handling imbalance data by assigning more weight to minority class and less weight to majority class. The limitation of this technique lied in that it generates weight according to class distribution of training data, thereby, creating dependency on input data. This leads to the lack of finding optimal weight at which good generalization performance could be achieved. This work uses Genetic Algorithm to find optimal weight which is given to minority and majority class instances.