Imbalanced big data classification model using social spider-cat swarm optimisation weighted incremental learning ensemble classifier

Abstract

Traditional strategies of data classification based on machine learning fail when handling highly imbalanced big data. This research proposes an incremental learning-based ensemble classifier for data imbalance classification. Initially, the big data are pre-processed, and the synthetic samples are then generated using the synthetic minority oversampling technique (SMOTE)-based data balancing strategy. The balanced big data is handled using the MapReduce architecture, which is inbuilt with the proposed incremental learning-based ensemble classifier comprising of artificial neural network (ANN), K-nearest neighbour (K-NN), support vector machine (SVM), decision tree (DT) and the naïve Bayes (NB) classifier. The class output for the proposed method is generated through the fusion parameter, which is decided using the proposed social spider-cat swarm optimisation (SSPCS) algorithm. The proposed method attained an accuracy of 94.37%, a sensitivity of 97%, and a specificity of 97%, which shows the effectiveness of the proposed strategy in imbalanced data classification.