SHREA: A Systematic Hybrid Resampling Ensemble Approach Using One Class Classifier

Imbalanced classification and data incompleteness are two critical issues in machine learning that, despite significant research, are difficult to solve. This paper presents the Systematic Hybrid Resampling Ensemble Approach that deals with the class imbalance and incompleteness of data at a given dataset and improves classification performance. We use an oscillator-guided Factor Based Multiple Imputation Oversampling technique to balance out the minority and majority data samples, while substituting missing values in the dataset. The improved dataset is an oversampled dataset and it goes through random undersample to create majority and minority class subsets. These subsets are then trained with the classifiers using one of the One Class Classifier-based methods, that is, One Class Support Vector Machine or Local Outlier Factor. Lastly, bootstrap aggregation ensemble setups are done using majority and minority class classifiers and combining them to come up with a score-based prediction. To mimic real-life scenarios where data could be missing, we introduce random missing values on each of these imbalance datasets to create $3$ new sets from each dataset with different missing values, that is, (10%, 20%, and 30%). The proposed method is experimented with using datasets taken from the KEEL website, and the results are compared against RBG, SBG, SBT, DTE, and EUS. Experimental analysis shows that the proposed approach gives better results revealing the efficiency and significance compared to the existing methods. The proposed method Local Outlier Factor Systematic Hybrid Resampling Ensemble Approach improves by 3.46%, 5.30%, 10.51% and 9.26% in terms of Recall, AUC, f-measure and g-mean and One Class Support Vector Machine Systematic Hybrid Resampling Ensemble Approach by 4.82%, 5.95%, 11.03% and 8.80% respectively.