Fuzzy Neighbors and Deep Learning-Assisted Spark Model for Imbalanced Classification of Big Data

Abstract

Big data is important in knowledge manipulation, assessment, and prediction. However, extracting and analyzing knowledge through big database are complex because of imbalance data distribution that leads to wrong decisions and biased classification outputs. Hence, an effective and optimal big data classification approach is designed using the proposed Bird Swarm Deer Hunting Optimization-Deep Belief Network (BSDHO-based DBN) algorithm based on spark architecture that follows the master and slave nodes. The proposed BSDHO is obtained by combining Deer Hunting Optimization algorithm and Bird Swarm Algorithm. The developed model poses two nodes, namely slave and master node. The training data is initially given to the master node in the spark architecture to perform transformation of data. Here, the transformation of data is done with an exponential log kernel, and then selection of feature is done with sequential forward selecting for choosing suitable features for enhanced processing. Consequently, oversampling process is performed with Fuzzy K-Nearest Neighbor (Fuzzy KNN) in the slave node using selected features to manage imbalance data. Then, in master node, classification is done with Deep belief Network, and trained using developed Bird swarm Deer Hunting Optimization (BSDHO) algorithm. On the other hand, the test data is taken as input, and is fed to the slave node to perform data transformation. Then, the transformed data is given to the master node for classification based on the proposed BSDHO. At last, the training data and testing data output produced the classified output. The proposed BSDHO-based DBN provided enhanced outcomes with highest specificity of 97.92%, accuracy of 96.92%, and sensitivity of 96.9%.