Weighted Feature Based Imperialist Competitive Algorithm With Ensemble Learning For Imbalanced Data Classification

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In recent trends, discovering classification knowledge from imbalanced data received has grabbed much interest by many researchers. Data set imbalancing might occur if any one class comprises considerably smaller number of examples than remaining classes. Various application greatly necessitates minority class which is regarded as quite interesting aspect. The imbalanced classes’ distribution set up a challenge for standard learning algorithms like k-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Neural Network (NN), subsequently biasing is done towards majority classes. An Improved Coefficient vector based Grey Wolf Optimization (ICGWO) Algorithm with ensemble classifier is deployed in previous approaches for classification. Nonetheless, adequate outcomes in terms of accuracy and execution time cannot be achieved. A Weighted Feature based Imperialist Competitive Algorithm (WFICA) with Ensemble Learning (EL) for imbalanced data classification is chiefly suggested for mitigating this issue. Primarily, normalization scheme is exploited for data transformation from different scales to an identical scale through Z-score normalization technique. Synthetic Minority Oversampling TEchnique (SMOTE) with Locally Linear Embedding (LLE) algorithm is deployed for oversampling process. Weighted Feature based Imperialist Competitive Algorithm (WFICA) is utilized for Optimal features selection which is done for classification accuracy enhancement. Ensemble Learning (EL) incorporated with Improved Bidirectional Long Short Term Memory (IBi-LSTM), Enhanced Weighted Support Vector Machine (EWSVM) and k-Nearest Neighbour (k-NN) classifiers is employed on selected features basis for performing classification. The suggested methodology is validated through experimental result and improved performance is attained when contrasted with prevailing system pertaining to accuracy, precision, recall and f-measure.