Efficient DANNLO classifier for multi-class imbalanced data on Hadoop

Abstract

In recent years, multi-class imbalance data classification is a major problem in big data. In such situations, we focused on developing a new Deep Artificial Neural Network Learning Optimization (DANNLO) Classifier for large collection of imbalanced data. In our proposed work, first the dataset reduction using principal component analysis for dimensionality reduction and initial centroid is computed. Then, parallel hierarchical pillar k-means clustering algorithm based on MapReduce is used to partitioning of an imbalanced data set into similar subset, which can improve the computational cost. The resultant clusters are given as input to the deep ANN for learning. In the next stage, deep neural network has been trained using the back propagation algorithm. In order to optimize the n-dimensional weight space, firefly optimization algorithm is used. Attractiveness and distance of each firefly is computed. Hadoop is used to handle these large volumes of variable size data. Imbalanced datasets is taken from ECDC (European Centre for Disease Prevention and Control) repository. The experimental results illustrated that the proposed method can significantly improve the effectiveness in classifying imbalanced data based on TP rate, F-measure, G-mean measures, confusion matrix, precision, recall, and ROC. The experimental results suggests that DANNLO classifier exceed other ordinary classifiers such as SVM and Random forest classifier on tested imbalanced data sets.