Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

Abstract

The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance problem, the random sampling methods (over and under sampling) being the most widely employed approaches. Moreover, sophisticated sampling methods have been developed, including the Synthetic Minority Over-sampling Technique (SMOTE), and also they have been combined with cleaning techniques such as Editing Nearest Neighbor or Tomek's Links (SMOTE+ENN and SMOTE+TL, respectively). In the big data context, it is noticeable that the class imbalance problem has been addressed by adaptation of traditional techniques, relatively ignoring intelligent approaches. Thus, the capabilities and possibilities of heuristic sampling methods on deep learning neural networks in big data domain are analyzed in this work, and the cleaning strategies are particularly analyzed. This study is developed on big data, multi-class imbalanced datasets obtained from hyper-spectral remote sensing images. The effectiveness of a hybrid approach on these datasets is analyzed, in which the dataset is cleaned by SMOTE followed by the training of an Artificial Neural Network (ANN) with those data, while the neural network output noise is processed with ENN to eliminate output noise; after that, the ANN is trained again with the resultant dataset. Obtained results suggest that best classification outcome is achieved when the cleaning strategies are applied on an ANN output instead of input feature space only. Consequently, the need to consider the classifier's nature when the classical class imbalance approaches are adapted in deep learning and big data scenarios is clear.

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

“…Imbalance data remains a key challenge against classification models [15,18]. The majority of literature considered re-sampling approaches, i.e., both over-sampling and under-sampling, to alleviate degradation due to the issue of imbalanced data [1,17,19,33,37].…”

“…The low volume of the potential target/important customer data (i.e., imbalanced data distribution) is a major challenge in extracting the latent knowledge in banks marketing data [1,3,10]. There is still an insisting need for handling the imbalanced dataset distribution reliably [15][16][17]; commonly used approaches [1,15,16,[18][19][20][21] impose processing overhead or lead to loss of information.…”

“…For a few decades, different interesting research efforts have attempted to improve understanding of the behavior of customers using ANNs [1,14,[22][23][24]26,27,31]. However, cost-sensitive algorithms have been with marginal interest to the researchers in bank marketing, while pre-processing the input dataset by re-sampling techniques to solve imbalanced class distribution have gained significant interest [1,15]. This research seeks unleashing the potentials of cost-sensitive analysis in providing enhanced bank marketing models, and reliable handling of imbalanced data distributions.…”

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

“…We used two established approaches, namely the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN), to balance the IoT-23, LITNET-2020, and NetML-2020 datasets [ 22 , 23 ]. Recently, hybrid approaches have become popular [ 24 ]. Methods like SMOTE+ENN, among other, have often been utilized for alleviating the issue of class imbalance to boost the efficiency of the classifier.…”

“…The count of samples in each class in the pre-processed dataset is subjected to the balancing procedure. Following [ 24 ], the approach for class balancing is presented in Algorithm 1. Algorithm 1 SMOTE+ENN.…”