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.

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

XGBoost is one of the popular machine learning models used in the domains like fraud detection as well as to tackle the class imbalance that creates overfitting if not handled properly. Digital transactions are encouraged by financial institutions to maintain data integrity. Credit card payment is one of the prevalent means of transactions carried out in both online and offline purchases. Consequently, the risk of fraudulent activities are increased during these financial transactions. This creates awareness among the researchers about the need for an efficient method to detect fraud accomplishments. This paper proposes an optimized XGBoost (OXGBoost) approach to handle class imbalance in the datasets without using resampling techniques. In this proposed approach, RandomizedSearchCV hyperparameter optimization technique is applied to find the optimal parameters of XGBoost. The data sampling techniques are integrated with XGBoost to increase the efficiency of the model. The experiment was performed based on the two realworld credit card datasets. The findings of the experiment proved that the integration of data sampling does not have an impact on the efficiency of XGBoost. Based on the comparison, the proposed approach has outperformed the higher accuracy.

Influence of Optimizing XGBoost to handle Class Imbalance in Credit Card Fraud Detection

Classifier ensemble methods in feature selection

The Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting maintenance time and cost and improving the product quality. However, faults in real industries are overwhelmingly outweighed by instances of good performance (faultless samples); this bias is reflected in the data captured by IoT devices. Imbalanced data limits the success of ML in predicting faults, thus presents a significant hindrance in the progress of smart manufacturing. Although various techniques have been proposed to tackle this challenge in general, this work is the first to present a framework for evaluating the effectiveness of these remedies in the context of manufacturing. We present a comprehensive comparative analysis in which we apply our proposed framework to benchmark the performance of different combinations of algorithm components using a real-world manufacturing dataset. We draw key insights into the effectiveness of each component and inter-relatedness between the dataset, the application context, and the design of the ML algorithm.

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Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis

Electrocardiographic (ECG) abnormalities are the most intuitive manifestation in the clinical diagnosis of cardiovascular disease. Although significant progress has been achieved in diagnosing ECG abnormalities, current convolutional neural network (CNN) based methods still show a relatively high rate of misdiagnosis. On the one hand, the ECG data an abnormality imbalance problem. On the other hand, the previous work did not consider the multi-label correlations of the ECG abnormalities. The main objective of this paper is to design an ECG abnormal event detection model based on multi-label correlation guided feature fusion. First, the proposed method calculates the correlation of different ECG abnormality labels on the basis of their frequency and Bayesian conditional probability and divides the ECG abnormalities into groups of different levels. The first-level ECG abnormalities are those that have the highest frequency and the second-level abnormalities are those that have a higher probability of other ECG abnormalities given the first-level ECG abnormalities, and so on. Then, the feature fusion strategy uses the multi-label correlation to guide the fusion of features of different levels. Specifically, the features of the first-level ECG signals are integrated into the features of the second-level ECG signals and the features of the former two-level ECG signals are integrated into the features of the third-level ECG signals. Furthermore, a multi-scale receptive field fusion module is proposed to enhance the multi-scale representation ability of the features. To evaluate the performance of the proposed method, it is validated against the 1st China physiological signal challenge dataset, and it achieved better results compared with other state-of-the-art methods. This proposed method achieves a precision score of 0.816, a sensitivity score of 0.845 and an F1-score of 0.827, which are better than other methods. The comparative analysis shows that the proposed method demonstrates the effectiveness in abnormal ECG diagnosis.

Multi-label correlation guided feature fusion network for abnormal ECG diagnosis

The class imbalance problem has become a leading challenge. Although conventional imbalance learning methods are proposed to tackle this problem, they have some limitations: 1) undersampling methods suffer from losing important information and 2) cost-sensitive methods are sensitive to outliers and noise. To address these issues, we propose a hybrid optimal ensemble classifier framework that combines density-based undersampling and cost-effective methods through exploring state-of-the-art solutions using multi-objective optimization algorithm. Specifically, we first develop a density-based undersampling method to select informative samples from the original training data with probability-based data transformation, which enables to obtain multiple subsets following a balanced distribution across classes. Second, we exploit the cost-sensitive classification method to address the incompleteness of information problem via modifying weights of misclassified minority samples rather than the majority ones. Finally, we introduce a multi-objective optimization procedure and utilize connections between samples to self-modify the classification result using an ensemble classifier framework. Extensive comparative experiments conducted on real-world data sets demonstrate that our method outperforms the majority of imbalance and ensemble classification approaches.

Xin Wen

Papers

Hybrid Classifier Ensemble for Imbalanced Data