Barricaded Boundary Minority Oversampling LS-SVM for a Biased Binary Classification

Classifying biased datasets with linearly non-separable features has been a challenge in pattern recognition because traditional classifiers, usually biased and skewed towards the majority class, often produce sub-optimal results. However, if biased or unbalanced data is not processed appropriately, any information extracted from such data risks being compromised. Least Squares Support Vector Machines (LS-SVM) is known for its computational advantage over SVM, however, it suffers from the lack of sparsity of the support vectors: it learns the separating hyper-plane based on the whole dataset and often produces biased hyper-planes with imbalanced datasets. Motivated to contribute a novel approach for the supervised classification of imbalanced datasets, we propose Barricaded Boundary Minority Oversampling (BBMO) that oversamples the minority samples at the boundary in the direction of the closest majority samples to remove LS-SVM’s bias due to data imbalance. Two variations of BBMO are studied: BBMO1 for the linearly separable case which uses the Lagrange multipliers to extract boundary samples from both classes, and the generalized BBMO2 for the non-linear case which uses the kernel matrix to extract the closest majority samples to each minority sample. In either case, BBMO computes the weighted means as new synthetic minority samples and appends them to the dataset. Experiments on different synthetic and real-world datasets show that BBMO with LS-SVM improved on other methods in the literature and motivates follow on research.