Imbalanced Data Classification Method Research Articles

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

This study introduces an interpretable imbalanced data classification method for detecting cryptocurrency transaction fraud. We address data imbalance using SMOTE oversampling and data augmentation through contrastive learning. Next, we introduce a Transformer-based deep learning model that learns sample relevance. The model undergoes pre-training with a contrastive loss and fine-tuning through Bayesian optimization to effectively extract high-dimensional, higher-order, and fraud-related features. We employ a SHAP-based interpreter along with attention scores to elucidate the role of various transaction features in fraud detection. Comparative results demonstrate the model's remarkable recall performance in identifying cryptocurrency transaction fraud. Furthermore, it achieves an excellent F1 value, striking a balance between accuracy and recall. This research not only enriches financial fraud detection but also enhances cryptocurrency transaction security, promotes market development, and contributes to economic stability and social security.

A [formula omitted]-means triangular synthesis large margin classifier with unified pinball loss for imbalanced data

Large Margin Distribution Machine (LDM) improves the Support Vector Machine (SVM) by integrating the marginal distribution of samples into the objective function, which exhibits excellent classification and generalization performance. However, most of the existing LDM-based models exhibit inherent flaws: (1) The assumption of a category uniform distribution, resulting in an inability to effectively address the issue of class imbalance. (2) The inheritance of the hinge loss in SVM, leading to inferior anti-noise performance. Given the shortcomings above, this paper constructs a novel K-means Triangular Synthesis (KTS) method for imbalanced data classification and introduces Unified Pinball (UP) loss to ensure robust performance, demonstrated as a novel KTS large margin classifier with UP Loss (KTS-UPLMC) model. Specifically, the KTS method utilizes the triangular synthesis and generates samples based on the cluster density, effectively mitigating the problems of introducing noise samples and strip-shaped sample distribution in the traditional synthetic minority oversampling technique, further alleviating the LDM’s classification line offset. In addition, the UP Loss considers quantile distance, improving the anti-noise performance of the model. Comparative experiments on artificial datasets and benchmark datasets validate the effectiveness and noise resistance of the proposed KTS-UPLMC in imbalanced data classification problems. Furthermore, the stability of the KTS-UPLMC is substantiated by the parameter sensitivity analysis experiment. In conclusion, the KTS method effectively addresses category imbalance, while the integration of the UP Loss enhances noise resistance.

MVQS: Robust multi-view instance-level cost-sensitive learning method for imbalanced data classification

Multi-view imbalanced learning is to handle the datasets with multi-view representations and imbalanced classes. Existing multi-view imbalanced learning methods can be divided into two main categories: multi-view ensemble learning and multi-view cost-sensitive learning. However, these methods suffer from the following problems: 1) neglecting either consensus or complementary information, 2) complex preprocessing and information fusion in multi-view ensemble learning and manual assignment of misclassification costs in multi-view cost-sensitive learning, and 3) limited ability to handle noisy samples. Therefore, we aim to design a concise and unified framework to grapple with the multi-view representations, imbalanced classes and noisy samples simultaneously. Inspired by the merits of support vector machine (SVM) and quadratic type squared error (QTSE) loss function, we propose a robust multi-view instance-level cost-sensitive SVM with QTSE loss (MVQS) for imbalanced data classification. The consensus regularization term and combination weight strategy are employed to fully exploit multi-view information. The QTSE loss can adaptively impose instance-level penalties to the misclassification of samples, and make MVQS be robust to noisy samples. We solve MVQS with the alternating direction method of multipliers (ADMM) and the gradient descent (GD) algorithm. Comprehensive experiments validate that MVQS is more competitive and robust than other benchmark approaches.

An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification

In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification.

A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare

In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data.

Imbalanced Data Classification Method Based on LSSASMOTE

Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper. First, the kernel function parameters and penalty parameters of the support vector machine (SVM) were optimized using levy sparrow search algorithm (LSSA), and a fitness function was correspondingly designed. Then, during the optimization process, SMOTE sampling rate was combined, and LSSA iteration was used to select the best combination of SVM parameters and SMOTE sampling rate. In addition, the oversampled samples were noise processed by Tomek Link. In this case, the LSSASMOTE+SVM classification model was constructed to classify the imbalanced data.Eight of the datasets used in the experiments were obtained on UCI and KEEL, and the other three datasets were created manually. The experimental results confirm that the model can effectively improve the classification accuracy of imbalanced data and can be used as a new imbalanced data classification method.

An improved GEV boosting method for imbalanced data classification with application to short-term rainfall prediction

This paper considers the imbalanced binary classification problem by focusing on the application of the short-term rainfall forecasting in arid and semi-arid regions. Specifically, we present a novel boosting-type method by utilizing the generalized extreme value (GEV) distribution as the link function and applying a gradient tree boosting algorithm to capture complex interactions among covariates. The proposed method has several appealing advantages such as, it can identify rare rainfall events as well as quantifying the uncertainties; it is data-driven that without any assumption on the relationship between the covariates and the rainfall event; the fitted model is highly interpretable, making it a useful tool for studying the rainfall mechanisms in arid and semi-arid regions. Experiments on two real-world datasets show that our approach outperforms its competing methods.

An improved and random synthetic minority oversampling technique for imbalanced data

Imbalanced data learning has become a major challenge in data mining and machine learning. Oversampling is an effective way to re-achieve the balance by generating new samples. However, most oversampling methods cannot perform well in the presence of noises and complicated distribution structures, very easy to generate redundant/unsafe/outlier samples. To handle this problem, we endeavor to propose a novel oversampling method, namely Improved and Random Synthetic Minority Oversampling Technique (IR-SMOTE). The core idea of IR-SMOTE is three-fold: (1) by applying an ascending operation to sort the majority class samples, noise samples in each cluster of minority class after k-means clustering are successfully removed; (2) the number of synthetic samples is adaptively assigned to each cluster in minority class by means of the kernel density estimation technique; and (3) based on the obtained attributes of the temporary synthetic samples in terms of random-SMOTE, a new synthesizing method is developed to generate new samples with a guaranteed diversity. Finally, many comparison experiments have been carried out on 18 well-known data sets, which illustrate the effectiveness and universal applicability of the proposed IR-SMOTE method for imbalanced data classification.

A Novel Imbalanced Data Classification Method Based on Weakly Supervised Learning for Fault Diagnosis

The class imbalance problem has a huge impact on the performance of diagnostic models. When it occurs, the minority samples are easily ignored by classification models. Besides, the distribution of class imbalanced data differs from the actual data distribution, which makes it difficult for classifiers to learn an accurate decision boundary. To tackle the above issues, this article proposes a novel imbalanced data classification method based on weakly supervised learning. First, Bagging algorithm is employed to sample majority data randomly to generate several relatively balanced subsets, which are then used to train several support vector machine (SVM) classifiers. Next, these trained SVM classifiers are adopted to predict the labels of those unlabeled data, and samples that are predicted as minority class are added to the original dataset to reduce the imbalance ratio. The critical idea of this article is to introduce real-world samples into the imbalanced dataset by virtue of weakly supervised learning. In addition, bidirectional gated recurrent units are used to construct a diagnostic model for fault diagnosis, and a new weighted cross-entropy function is proposed as the loss function to reduce the impact of noise. Besides, it also increases the model's attention to the original minority samples. Furthermore, experimental evaluations of the proposed method are conducted on two datasets, i.e., Prognostics and Health Management challenge 2008 and 2010 datasets, and the experimental results demonstrate the effectiveness and superiority of the proposed method.

Adaptive Fusion Based Method for Imbalanced Data Classification.

The imbalance problem is widespread in real-world applications. When training a classifier on the imbalance datasets, the classifier is hard to learn an appropriate decision boundary, which causes unsatisfying classification performance. To deal with the imbalance problem, various ensemble algorithms are proposed. However, conventional ensemble algorithms do not consider exploring an effective feature space to further improve the performance. In addition, they treat the base classifiers equally and ignore the different contributions of each base classifier to the ensemble result. In order to address these problems, we propose a novel ensemble algorithm that combines effective data transformation and an adaptive weighted voting scheme. First, we utilize modified metric learning to obtain an effective feature space based on imbalanced data. Next, the base classifiers are assigned different weights adaptively. The experiments on multiple imbalanced datasets, including images and biomedical datasets verify the superiority of our proposed ensemble algorithm.

Feature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted K-nearest neighbors

Most existing imbalanced data classification models mainly focus on the classification performance of majority class samples, and many clustering algorithms need to manually specify the initial cluster centers and the number of clusters. To solve these drawbacks, this study presents a novel feature reduction method for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors (AWKNN). First, the similarity between samples is evaluated by the difference and smaller value between samples on each dimension, a similarity measure matrix is then developed to measure the similarity between clusters, after which a new hierarchical clustering model is constructed. By combining the cluster center of each sample cluster with its nearest neighbor, new samples are generated. Then, a hybrid sampling model based on similarity measure is presented by putting the generated samples into imbalanced data and removing samples from majority classes. Thus, a balanced decision system is constructed based on generated samples and minority class samples. Second, to address the issues that the traditional symmetric uncertainty only considers the correlation between features, and mutual information ignores the added information after classification, the normalized information gain is introduced to design new symmetric uncertainty between each feature and the other features; then, the ordered sequence and the average of the symmetric uncertainty difference of each feature are provided to adaptively select the k-nearest neighbors of features. Moreover, the weight of the k-th nearest neighbor of features is defined to present the AWKNN density of features and their ordered sequence for clustering features. Finally, by combining the weighted average redundancy with the symmetric uncertainty between features and decision classes, the maximum relevance between each feature and decision classes, and the minimum redundancy among features in the same cluster is presented to select the optimal feature subset from the feature clusters. Experiments applied to 29 imbalanced datasets show that the developed algorithm is effective and can select the optimal feature subset with high classification accuracy for imbalanced data.

Reprint: A Randomized Extrapolation Based on Principal Components for Data Augmentation

Data scarcity and data imbalance have attracted a lot of attention in many fields. Data augmentation, explored as an effective approach to tackle them, can improve the robustness and efficiency of classification models by generating new samples. This paper presents REPRINT, a simple and effective hidden-space data augmentation method for imbalanced data classification. Given hidden-space representations of samples in each class, REPRINT extrapolates, in a randomized fashion, augmented examples for target class by using subspaces spanned by principal components to summarize distribution structure of both source and target class. Consequently, the examples generated would diversify the target while maintaining the original geometry of target distribution. Besides, this method involves a label refinement component which allows to synthesize new soft labels for augmented examples. Compared with different NLP data augmentation approaches under a range of data imbalanced scenarios on four text classification benchmark, REPRINT shows prominent improvements. Moreover, through comprehensive ablation studies, we show that label refinement is better than label-preserving for augmented examples, and that our method suggests stable and consistent improvements in terms of suitable choices of principal components. Moreover, REPRINT is appealing for its easy-to-use since it contains only one hyperparameter determining the dimension of subspace and requires low computational resource.

Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal’s, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.

Bound estimation-based safe acceleration for maximum margin of twin spheres machine with pinball loss

Maximum margin of twin spheres support vector machine (MMTSSVM) is an efficient method for imbalanced data classification. As an extension to enhance noise insensitivity of MMTSSVM, MMTSSVM with pinball loss (Pin-MMTSM) has a good generalization performance. However, it is not efficient enough for large-scale data. Inspired by the sparse solution of SVMs, in this paper, we propose a safe accelerative approach to reduce the computational cost. Unlike the existing safe screening rules, where only one variable changes with the parameters. We utilize bound estimation-based to derive the upper and lower bounds of center and radius. With our approach, the inactive samples are discarded before solving the problem, thus it can reduce the computational cost. One important advantage of our approach is safety, i.e., we can obtain the same solution as solving original problem both in linear and non-linear cases. Moreover, it is obvious that our acceleration approach is independent of the solver. To further accelerate the computational speed, a decomposition method is employed. Experiments on three artificial datasets and twelve benchmark datasets clearly demonstrate the effectiveness of our approach. At last, we extend bound estimation-based method to ν-SVM, theoretical analysis and experimental results both verify its feasibility and effectiveness.

Penalized multiple distribution selection method for imbalanced data classification

In reality, the amount of data from different categories varies significantly, which results in learning bias towards prominent classes, hindering the overall classification performance. In this paper, by proving that traditional classification methods that use single softmax distribution are limited for modeling complex and imbalanced data, we propose a general Multiple Distribution Selection (MDS) method for imbalanced data classification. MDS employs a mixture distribution that is composed of a single softmax distribution and a set of degenerate distributions to model imbalanced data. Furthermore, a dynamic distribution selection method, based on L1 regularization, is also proposed to automatically determine the weights of distributions. In addition, the corresponding two-stage optimization algorithm is designed to estimate the parameters of models. Extensive experiments conducted on three widely used benchmark datasets (IMDB, ACE2005, 20NewsGroups) show that our proposed mixture method outperforms previous methods. Moreover, under highly imbalanced setting, our method achieves up to a 4.1 absolute F1 gain over high-performing baselines.

A gravitational density-based mass sharing method for imbalanced data classification

Classification is one of the most popular branches of supervised learning algorithms. In the real-world problems, there are some situations in which distributions of the two classes are not the same. This situation is known as a class imbalanced problem. In the past years, several investigations have been done to find a way of handling imbalanced data, which most of them stay in one of two groups, including internal techniques and external techniques. The proposed gravitational density-based mass sharing method (GDMS) is an internal method that is designed based on k-nearest neighbor and fixed radius nearest neighbor (FRNN) rules. GDMS is a new technique that assigns masses to instances based on their local density while considering the global information too. In the labeling phase, GDMS decides based on the sum of gravitational forces coming from the candidates set, which are defined by FRNN rule. The GDMS does not need any parameters to be set in the whole procedure of classification, which is an advantage in comparison with the previous methods. To demonstrate the effectiveness of our proposed method, we use 40 standard datasets from the KEEL repository. The results show the effectiveness and superiority of GDMS compared to the competing methods.

An Ensemble Learning Imbalanced Data Classification Method Based on Sample Combination Optimization

Imbalanced data classification is one of the hot topics in data mining and machine learning in recent years. In practice, Imbalanced data classification is very common, such as cancer detection, spam discrimination, credit card fraud detection, etc. Because of the large difference in the number of categories and Imbalanced distribution, traditional classification algorithms have poor classification effect on minority classes, and correct identification of minority classes often brings greater value. Therefore, how to effectively identify minority classes in Imbalanced data is of great importance. Practical significance. Aiming at the problems that the Bagging-based Imbalanced data classification method cannot guarantee the validity and existence of classification boundaries by adding redundant noise information and sampling, an ensemble learning GABagging method based on sample combination optimization is proposed. Firstly, the sample combination optimization algorithm uses genetic algorithm to select a subset from most classes and construct a new data set with a few classes. Subsequently, several sample combinatorial optimization algorithms are used to train and integrate several classifiers. The experimental results show that GABagging can improve the correct recognition ability of minority classes on 19 Imbalanced datasets compared with other similar methods such as TPR and AUC, without excessive loss of recognition ability of majority classes. It is proved that GABagging can compensate for the shortcomings of related Bagging-based methods such as easy loss, increasing samples and not guaranteeing the validity and existence of classification boundaries after sampling.

CoGBUS- Center of Gravity based under Sampling Method for Imbalanced Data Classification

Learning of class imbalanced data becomes a challenging issue in the machine learning community as all classification algorithms are designed to work for balanced datasets. Several methods are available to tackle this issue, among which the resampling techniques- undersampling and oversampling are more flexible and versatile. This paper introduces a new concept for undersampling based on Center of Gravity principle which helps to reduce the excess instances of majority class. This work is suited for binary class problems. The proposed technique –CoGBUS- overcomes the class imbalance problem and brings best results in the study. We take F-Score, GMean and ROC for the performance evaluation of the method.

An embedded feature selection method for imbalanced data classification

Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority class is a critically important issue. Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index &#x0028 WGI &#x0029 is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve &#x0028 ROC AUC &#x0029 and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of Fmeasure achieves excellent performance only if 20 &#x0025 or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.