Ensemble Model with Improved DCNN for Big data Classification by Handling Class Imbalance Problem

This research suggests a big data classification model that uses an improved deep convolutional neural network (IDCNN) and has five phases. In the first stage, Z-score normalisation is employed for preprocessing the input data. The second phase involves processing the preprocessed data for improved class imbalance using SMOTE-ENC. Then, the subsequent phase involves extracting the collection of features, which also includes raw data and features based on correlation, entropy, and MI. Then, in the fourth phase, to guarantee appropriate feature selection, an improved recursive feature elimination (IRFE) approach is employed for the selection of features is performed using the extracted features. Finally, ensemble classification using a collection of classifiers like Bi-LSTM, SVM, RNN and IDCNN is performed depending on the features that have been chosen. The IDCNN classifier is used in this case to categorise the final result by taking Bi-LSTM, SVM and RNN output scores as input.