Application of Binary Whale Optimization Algorithm for Solving Imbalanced Data Problems
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Abstract
This research is aimed at developing a novel undersampling algorithm by combining the ideas of the whale and binary whale optimization algorithms with K nearest neighbor classification, in order to solve imbalanced data problems. Twelve datasets of varying imbalance ratios ranging from 1.82 to 42.01 were selected from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and also the imbalancedlearn repository, to be used in the evaluation of the novel algorithm. This research work started by splitting each dataset into two parts, the training set and the testing set. Whereas the minority class of each training set remained untouched, its majority class was processed by the proposed algorithm with the parameter in K-nearest neighbor classification fixed to K = 1, to obtain an optimal representative subset of the majority class. Then a support vector machine classifier was trained with the new and reduced training set for performance assessment. It was found that the proposed algorithm had best overall performance when compared with another three undersampling methods, namely random undersampling, cluster centroid, and near-miss algorithms, showing average efficiency measurement results as follows: Accuracy = 0.8596, F1 score = 0.6255, G-mean = 0.8941, AUROC = 0.9363, AUPRC = 0.6978, Sensitivity = 0.9444, Precision = 0.5271, MCC = 0.6204, and Kappa = 0.5695.
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