Feature Selection and Ensemble Learning Techniques in One-Class Classifiers: An Empirical Study of Two-Class Imbalanced Datasets

Class imbalance learning is an important research problem in data mining and machine learning. Most solutions including levels, algorithm cost sensitive approaches are derived using multi-class classifiers, depending on the number of classes to be classified. One-class classification (OCC) techniques, contrast, have been widely used for anomaly or outlier detection where only normal positive class training available. In this study, we treat every two-class imbalanced dataset as problem, which contains a larger majority class, i.e. very small minority class. The objectives paper understand performance OCC classifiers examine level improvement when feature selection considered pre-processing ensemble employed combine multiple classifiers. Based 55 datasets with different ranges ratios one-class support vector machine, isolation forest, local factor representative found that good at high ratio datasets, outperforming C4.5 baseline. most cases, though, performing does not improve most. However, many homogeneous heterogeneous classifier ensembles do outperform single some specific combinations both without selection, similar better than baseline combination SMOTE C4.5.