Class Imbalance Classification Research Articles

Propensity score oversampling and matching for uplift modeling

In this paper, we propose a novel matching strategy to correct for confounding in uplift modeling. Our method, called propensity score oversampling and matching (ProSOM), extends the well-known propensity score matching (PSM) technique by addressing one of its main limitations: dealing with small datasets that face an imbalance in the distribution of the causal variable. Apart from this, we also face the additional complexity of dealing with class labels. The proposed method establishes a parallel between uplift modeling and class-imbalance classification as it extends existing oversampling techniques to create synthetic elements from the treatment group. We design an algorithm that performs classaware data oversampling in the treatment group, and then it matches samples from this group with the control group. This can be seen as a novel hybrid undersampling-oversampling solution for causal learning. Experiments on five datasets show the virtues of ProSOM in terms of predictive performance, achieving the best Qini coefficient for all five datasets in relation to PSM and other resampling solutions.

Regularized Discrete Optimal Transport for Class-Imbalanced Classifications

Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that leverages optimal transport for handling imbalanced data. Specifically, we establish a transport plan between training and testing data without modifying the original data distribution, drawing upon the principles of optimal transport theory. Additionally, we introduce a non-convex interclass regularization term to establish connections between testing samples and training samples with the same class labels. This regularization term forms the basis of a regularized discrete optimal transport model, which is employed to address imbalanced classification scenarios. Subsequently, in line with the concept of maximum minimization, a maximum minimization algorithm is introduced for regularized discrete optimal transport. Subsequent experiments on 17 Keel datasets with varying levels of imbalance demonstrate the superior performance of the proposed approach compared to 11 other widely used techniques for class-imbalanced classification. Additionally, the application of the proposed approach to water quality evaluation confirms its effectiveness.

AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals.

Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.

Semi-supervised imbalanced classification of wafer bin map defects using a Dual-Head CNN

Wafer Bin Map (WBM) defect patterns are a critical aspect of identifying the root cause of manufacturing defects in the semiconductor industry. Semi-supervised learning (SSL) approaches have gained popularity for this purpose, as they can leverage both labeled and unlabeled data to improve model performance. However, SSL of WBM defect patterns is challenging due to class imbalance, where some defect classes have many more examples than others. Most of the existing SSL approaches assume a balanced dataset and often fail to provide satisfactory results when applied to imbalanced class problems. To address this issue, this work proposes a novel Dual-Head Convolutional Neural Network (CNN) architecture that contains two classifier heads. One classifier head maximizes overall classification scores, while the other aims to maximize per-class classification scores, providing equal attention to both majority and minority classes. The proposed CNN architecture uses pseudo-labels selected based on the outputs of these two classifiers to expand the labeled training set, which is then used to retrain the CNN. In this way, highly confident pseudo-labels are selected even from the minority classes, leading to better model training. Experiments show that the proposed approach is effective in handling class-imbalanced classification of WBM defect patterns, reporting state-of-the-art classification with an F1 score of 0.918, accuracy of 98.2% and a mean per-class accuracy of 91.7% using a lightweight ResNet-10 model as the backbone on the real-world public WBM dataset, WM-811K. The proposed approach’s success suggests that it could be a valuable tool for improving the accuracy and reliability of WBM defect pattern classification in semiconductor manufacturing. The code is available at https://github.com/M-Siyamalan/SSL-DHCNN.

Data-level Hybrid Strategy Selection for Disk Fault Prediction Model Based on Multivariate Gan

Data class imbalance is a common problem in classification problems, where minority class samples are often more important and more costly to misclassify in a classification task. Therefore, it is very important to solve the data class imbalance classification problem. The SMART dataset exhibits an evident class imbalance, comprising a substantial quantity of healthy samples and a comparatively limited number of defective samples. This dataset serves as a reliable indicator of the disc's health status. In this paper, we obtain the best balanced disk SMART dataset for a specific classification model by mixing and integrating the data synthesised by multivariate generative adversarial networks (GAN) to balance the disk SMART dataset at the data level; and combine it with genetic algorithms to obtain higher disk fault classification prediction accuracy on a specific classification model.

Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset

The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because their breast tissue is generally denser than the older women. The Breast Cancer Wisconsin image dataset contains two classes: (i) Benign (Minority class), (ii) Malignant (Majority class). The imbalanced class distribution leads to a deterioration in the classifier model performance owing to the biased classification towards the majority class. Therefore, in this article, an improved generative adversarial network (I-GAN) is proposed to overcome the class imbalance problem. Here, the proposed method is the consolidation of deep convolutional generative adversarial network (DCIGAN) and modified convolutional neural network, (MCNN), therefore it is known as DCIGAN-MCNN method. First, the DCIGAN is utilized for balancing the dataset by generating more samples in the training dataset. Then, this training dataset based the classification of Breast cancer is developed using the modified convolutional neural network. The proposed method is executed in MATLAB. The performance analysis are carried out in Breast Cancer Wisconsin (Prognostic) Data Set provides Maximum Geometry Mean (MGM) as 24.058%, 9.582%, Matthews’s correlation coefficient (MCC) as 78.623%, 30.357% higher than the existing methods, like CI-BC-RK-SVM, CI-BC-GA, CI-BC-DC-CNN, CI-BC-RF and CI-BC-BMIC-Net respectively. Finally, the simulation results prove that the proposed method can be able to find the optimal solutions efficiently and accurately.

An ensemble oversampling method for imbalanced classification with prior knowledge via generative adversarial network

Currently, an increasing number of real-world applications show characteristics of class-imbalance classification suffering from severe class distribution skewing, thus requiring brand new algorithms to learn from imbalanced datasets. In this paper, a novel oversampling method using GAN framework is proposed for numerical imbalanced data, namely G-GAN. In the method, a Gaussian distribution of minority samples is estimated to get prior knowledge of minority class for the latent space of GAN. In order to increase the randomness of the generated samples, noises are obtained by a mixed strategy, that is, some noises of generator obey Gaussian distribution and others obey random distribution. Then G-GAN is trained to generate dispersive positive samples with the idea of Bagging, which could avoid the occurrence of overfitting. G-GAN is different from other literatures in that GAN does not directly generate minority samples, but adds the distribution information of minority samples to the latent space of GAN, and then generates minority samples. Compared with 11 commonly used oversampling methods, G-GAN obtains promising results in terms of G-mean, AUC, F-measure and ROC utilizing three classifiers on 11 benchmark imbalanced datasets. Furthermore, G-GAN is also validated on AUC metrics of a real Diabetes imbalanced dataset. The results demonstrate that G-GAN can provide great potential for imbalanced classification in the two numerical experiments.

Relative Density-Based Intuitionistic Fuzzy SVM for Class Imbalance Learning

The support vector machine (SVM) has been combined with the intuitionistic fuzzy set to suppress the negative impact of noises and outliers in classification. However, it has some inherent defects, resulting in the inaccurate prior distribution estimation for datasets, especially the imbalanced datasets with non-normally distributed data, further reducing the performance of the classification model for imbalance learning. To solve these problems, we propose a novel relative density-based intuitionistic fuzzy support vector machine (RIFSVM) algorithm for imbalanced learning in the presence of noise and outliers. In our proposed algorithm, the relative density, which is estimated by adopting the k-nearest-neighbor distances, is used to calculate the intuitionistic fuzzy numbers. The fuzzy values of the majority class instances are designed by multiplying the score function of the intuitionistic fuzzy number by the imbalance ratio, and the fuzzy values of minority class instances are assigned the intuitionistic fuzzy membership degree. With the help of the strong capture ability of the relative density to prior information and the strong recognition ability of the intuitionistic fuzzy score function to noises and outliers, the proposed RIFSVM not only reduces the influence of class imbalance but also suppresses the impact of noises and outliers, and further improves the classification performance. Experiments on the synthetic and public imbalanced datasets show that our approach has better performance in terms of G-Means, F-Measures, and AUC than the other class imbalance classification algorithms.

Grouping-based Oversampling in Kernel Space for Imbalanced Data Classification

The class-imbalanced classification is a difficult problem because not only traditional classifiers are more biased towards the majority classes and inclined to generate incorrect predictions, but also the existing algorithms often have difficulty tackling this kind of problem with the class overlapping. Oversampling is a widely used and effective method to obtain balanced samples for imbalanced data, but the existing oversampling methods usually result in more serious class overlapping due to improper choice of the reference samples. To circumvent this shortcoming, according to the different possibilities of minority class samples appearing in the overlapping regions in the feature space, a grouping scheme for the minority class samples is first designed to identify the overlapping region samples. Then, a new oversampling method based on this grouping scheme is proposed to make the new samples far away from the overlapping region and rectify the decision boundary properly. Subsequently, a new effective classification algorithm is developed for imbalanced data. Extensive experiments show that the proposed algorithm is superior to the seventeen benchmark algorithms in terms of three performance metrics, especially on high imbalance ratio data sets.

Provably training overparameterized neural network classifiers with non-convex constraints

Training a classifier under non-convex constraints has gotten increasing attention in the machine learning community thanks to its wide range of applications such as algorithmic fairness and class-imbalanced classification. However, several recent works addressing non-convex constraints have only focused on simple models such as logistic regression or support vector machines. Neural networks, one of the most popular models for classification nowadays, are precluded and lack theoretical guarantees. In this work, we show that overparameterized neural networks could achieve a near-optimal and near-feasible solution of non-convex constrained optimization problems via the project stochastic gradient descent. Our key ingredient is the no-regret analysis of online learning for neural networks in the overparameterization regime, which may be of independent interest in online learning applications.

Improving the Performance of an Associative Classifier in the Context of Class-Imbalanced Classification

Class imbalance remains an open problem in pattern recognition, machine learning, and related fields. Many of the state-of-the-art classification algorithms tend to classify all unbalanced dataset patterns by assigning them to a majority class, thus failing to correctly classify a minority class. Associative memories are models used for pattern recall; however, they can also be employed for pattern classification. In this paper, a novel method for improving the classification performance of a hybrid associative classifier with translation (better known by its acronym in Spanish, CHAT) is presented. The extreme center points (ECP) method modifies the CHAT algorithm by exploring alternative vectors in a hyperspace for translating the training data, which is an inherent step of the original algorithm. We demonstrate the importance of our proposal by applying it to imbalanced datasets and comparing the performance to well-known classifiers by means of the balanced accuracy. The proposed method not only enhances the performance of the original CHAT algorithm, but it also outperforms state-of-the-art classifiers in four of the twelve analyzed datasets, making it a suitable algorithm for classification in imbalanced class scenarios.

Hybrid SVM-CNN Classification Technique for Human-Vehicle Targets in an Automotive LFMCW Radar.

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the score of and area under the curve (AUC) of the receiver operating characteristic (ROC) of over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar.

Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081).

Machine Learning has been on the rise and healthcare is no exception to that. In healthcare, mental health is gaining more and more space. The diagnosis of mental disorders is based upon standardized patient interviews with defined set of questions and scales which is a time consuming and costly process. Our objective was to apply the machine learning model and to evaluate to see if there is predictive power of biomarkers data to enhance the diagnosis of depression cases. In this research paper, we aimed to explore the detection of depression cases among the sample of 11,081 Dutch citizen dataset. Most of the earlier studies have balanced datasets wherein the proportion of healthy cases and unhealthy cases are equal but in our study, the dataset contains only 570 cases of self-reported depression out of 11,081 cases hence it is a class imbalance classification problem. The machine learning model built on imbalance dataset gives predictions biased toward majority class hence the model will always predict the case as no depression case even if it is a case of depression. We used different resampling strategies to address the class imbalance problem. We created multiple samples by under sampling, over sampling, over-under sampling and ROSE sampling techniques to balance the dataset and then, we applied machine learning algorithm “Extreme Gradient Boosting” (XGBoost) on each sample to classify the mental illness cases from healthy cases. The balanced accuracy, precision, recall and F1 score obtained from over-sampling and over-under sampling were more than 0.90.

ACO Resampling: Enhancing the performance of oversampling methods for class imbalance classification

Many sampling-based preprocessing methods have been proposed to solve the problem of unbalanced dataset classification. The fundamental principle of these methods is rebalancing an unbalanced dataset by a concrete strategy. Herein, we introduce a novel hybrid proposal named ant colony optimization resampling (ACOR) to overcome class imbalance classification. ACOR primarily includes two steps: first, it rebalances an imbalanced dataset by a specific oversampling algorithm; next, it finds an (sub)optimal subset from the balanced dataset by ant colony optimization. Unlike other oversampling techniques, ACOR does not focus on the mechanics of generating new samples. The main advantage of ACOR is that existing oversampling algorithms can be fully utilized and an ideal training set can be obtained by ant colony optimization. Therefore, ACOR can enhance the performance of existing oversampling algorithms. Experimental results on 18 real imbalanced datasets prove that ACOR yields significantly better results compared with four popular oversampling methods in terms of various assessment metrics, such as AUC, G-mean, and BACC.

Reduct ECOC Framework for Network Intrusion Detection System

Now a day’s network security is major concern for e-government and e-commerce applications. A wide range of malicious activities are increasing with the usage of internet and network technologies. Identifying novel threats and finding modern solutions for network to prevent from these threats are important. Designing an effective intrusion detection system is significant to continuously look out the network activities to efficiently thwart malicious attacks or to identify the intruders. To tackle multi class imbalance classification problem in networks, a reduct based ECOC ensemble framework for NIDS is proposed to efficiently identify attacks in a multi class scenario. The Reduct-ECOC classifier is validated on highly imbalanced benchmark NSL-KDD intrusion datasets as well as other UCI-ML datasets. The experimental results on eight highly imbalanced datasets show that Reduct-ECOC classifier performs better than many other state-of-art multi-class classification ECOC learning methods.

An effective noise reduction technique for class imbalance classification

An effective noise reduction technique for class imbalance classification

Ss-InfoGAN for Class-Imbalance Classification of Bearing Faults

As the core part of the Prognostic and Health Management (PHM) of major equipment such as high-speed trains and aero engines, bearing fault classification have been the research priorities in the field. Although convolutional neural network (CNN) has shown good results in this type of task, the real application with limited training data makes CNN have a big gap between the actual application and the expected effect. Therefore, bearing faults classification with class-imbalance is a very practical work. In this paper, semi-supervised information maximizing generative adversarial network (ss-InfoGAN), which uses adversarial structure to generate samples of the minority, is introduced to augment data to solve class imbalance problem. In addition, the latent codes, the inputs of generator, are decomposed into three parts with three additional networks, respectively, at the start of generator. Meanwhile, the 50% precision threshold is proposed during the training stage of discriminator to make a trade-off between computing resources and theoretical foundations and facilitate the network converge. Bearing fault experiments are conducted to investigate the effectiveness of the presented network. The result shows classification accuracy is improved by 40% by the ss-InfoGAN compared to the traditional CNN for the case of extremely class-imbalance condition.

KA-Ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling

Imbalanced learning has become a research emphasis in recent years because of the growing number of class-imbalance classification problems in real applications. It is particularly challenging when the imbalanced rate is very high. Sampling, including under-sampling and over-sampling, is an intuitive and popular way in dealing with class-imbalance problems, which tries to regroup the original dataset and is also proved to be efficient. The main deficiency is that under-sampling methods usually ignore many majority class examples while over-sampling methods may easily cause over-fitting problem. In this paper, we propose a new algorithm dubbed KA-Ensemble ensembling under-sampling and over-sampling to overcome this issue. Our KA-Ensemble explores EasyEnsemble framework by under-sampling the majority class randomly and over-sampling the minority class via kernel based adaptive synthetic (Kernel-ADASYN) at meanwhile, yielding a group of balanced datasets to train corresponding classifiers separately, and the final result will be voted by all these trained classifiers. Through combining under-sampling and over-sampling in this way, KA-Ensemble is good at solving class-imbalance problems with large imbalanced rate. We evaluated our proposed method with state-of-the-art sampling methods on 9 image classification datasets with different imbalanced rates ranging from less than 2 to more than 15, and the experimental results show that our KA-Ensemble performs better in terms of accuracy (ACC), F-Measure, G-Mean, and area under curve (AUC). Moreover, it can be used in both dichotomy and multi-classification on both image classification and other class-imbalance problems.

Measuring harmfulness of class imbalance by data complexity measures in oversampling methods

Many real world applications consist of skewed datasets which result in class imbalance problem. During classification, class imbalance cause underestimation of minority classes. Researchers have proposed a number of algorithms to deal with this problem. But recent research studies have shown that some skewed datasets are unharmful and applying class imbalance algorithms on these datasets lead to degenerated performance and increased execution time. In this research paper, we have pre-estimated the degree of harmfulness of class imbalance for skewed classification problems, using two of the data complexity measures: scatter matrix based class separability measure and ratio of intra-class versus inter-class nearest neighbours. Also the performance of oversampling-based class imbalance classification algorithms have been analysed with respect to these data complexity measures. The experiments are conducted using k-nearest neighbour (k-nn) and naive Bayes as the base classifiers for this study. The obtained results illustrate the usefulness of these measures by providing the prior information about the nature of the imbalance datasets that help us to select the more efficient classification algorithm.

Research on classification method of high-dimensional class-imbalanced datasets based on SVM

High-dimensional problems result in bad classification results because some combinations of features have an adverse effect on classification; while class-imbalanced problems make the classifier to concern the majority class more but the minority less, because the number of samples of majority class is more than minority class. The problem of both high-dimensional and class-imbalanced classification is found in many fields such as bioinformatics, healthcare and so on. Many researchers study either the high-dimensional problem or class-imbalanced problem and come up with a series of algorithms, but they ignore the above new problem, which indicates high-dimensional problems affect sampling process while class-imbalanced problems interfere feature selection. Firstly, this paper analyses the new problem arising from the mutual influence of the two problems, and then introduces SVM and analyses its advantages in dealing high-dimensional problem and class-imbalanced problem. Next, this paper proposes a new algorithm named BRFE-PBKS-SVM aimed at high-dimensional class-imbalanced datasets, which improves SVM-RFE by considering the class-imbalanced problem in the process of feature selection, and it also improves SMOTE so that the procedure of over-sampling could work in the Hilbert space with an adaptive over-sampling rate by PSO. Finally, the experimental results show the performance of this algorithm.