Classification Of Imbalanced Datasets Research Articles

An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset

Diabetes is a prevalent chronic condition that poses significant challenges to early diagnosis and identifying at-risk individuals. Machine learning plays a crucial role in diabetes detection by leveraging its ability to process large volumes of data and identify complex patterns. However, imbalanced data, where the number of diabetic cases is substantially smaller than non-diabetic cases, complicates the identification of individuals with diabetes using machine learning algorithms. This study focuses on predicting whether a person is at risk of diabetes, considering the individual’s health and socio-economic conditions while mitigating the challenges posed by imbalanced data. We employ several data augmentation techniques, such as oversampling (Synthetic Minority Over Sampling for Nominal Data, i.e.SMOTE-N), undersampling (Edited Nearest Neighbor, i.e. ENN), and hybrid sampling techniques (SMOTE-Tomek and SMOTE-ENN) on training data before applying machine learning algorithms to minimize the impact of imbalanced data. Our study sheds light on the significance of carefully utilizing data augmentation techniques without any data leakage to enhance the effectiveness of machine learning algorithms. Moreover, it offers a complete machine learning structure for healthcare practitioners, from data obtaining to machine learning prediction, enabling them to make informed decisions.

Classification of Major Solar Flares from Extremely Imbalanced Multivariate Time Series Data Using Minimally Random Convolutional Kernel Transform

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares, ranging from electronic communication disruption to radiation exposure-based health risks to astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MiniRocket), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations Sequence Learner (Mr-SEQL), and a Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over nine years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the True Skill Statistic (TSS) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MiniRocket, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics.

An Iterative Resampling Deep Decoupling Domain Adaptation method for class-imbalance bearing fault diagnosis under variant working conditions

Bearing fault diagnosis suffers from unsatisfied accuracy under variant working conditions, due to class-imbalance and distribution discrepancy of faulty data. Class-imbalance will cause classification bias that makes the diagnostic model tend to misclassify faulty classes, while the distribution discrepancy caused by variant working conditions will further reduce the model’s generalization. Thus, this paper is mainly focusing on solving the two challenges simultaneously, and an Iterative Resampling Deep Decoupling Domain Adaptation (IRDDDA) model is proposed. The model consists of a feature extractor, domain classifier, label classifier and feature resampler modules, and it adopts a decoupling two-stage training framework. In the first stage, the model learns domain invariant features from the source domain and target domain based on original class-imbalance datasets. In the second stage, the feature extractor is partially fixed, and the feature-based resampling method is used to adjust the biased classifier caused by the class-imbalance problem. An iterative training strategy is adopted to simultaneously update the upper-layers of the feature extractor and adjust the classifier till convergence. The method is verified on the CWRU open dataset and the dataset collected from our bearing test rig. The verification results show that our method can achieve a better fault diagnostic performance overall, especially in minority faulty classes.

Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.

Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermore, the computational cost of inspecting each pixel is expensive because the number of pixels is high even at low resolution. In this work, we propose a hybrid image processing method. Simple Linear Iterative Clustering with Gaussian Filter(SLIC-G) for the purpose of overcoming pixel constraints. The SLIC-G image processing method is divided into two stages: (1) simple linear iterative clustering superpixel segmentation and (2) Gaussian smoothing operation. In such a way, a large number of new transformed datasets are generated and then used for model training. Finally, two performance evaluation metrics that are suitable for imbalanced diabetic retinopathy datasets were used to validate the effectiveness of the proposed SLIC-G. The results indicate that, in comparison to prior published works' results, the proposed SLIC-G shows better performance on image classification of class imbalanced diabetic retinopathy datasets. This research reveals the importance of image processing and how it influences the performance of deep learning networks. The proposed SLIC-G enhances pre-trained network performance by eliminating the local redundancy of an image, which preserves local structures, but avoids over-segmented, noisy clips. It closes the research gap by introducing the use of superpixel segmentation and Gaussian smoothing operation as image processing methods in diabetic retinopathy-related tasks.

Investigation of Imbalanced Sentiment Analysis in Voice Data: A Comparative Study of Machine Learning Algorithms

INTRODUCTION: Language serves as the primary conduit for human expression, extending its reach into various communication mediums like email and text messaging, where emoticons are frequently employed to convey nuanced emotions. In the digital landscape of long-distance communication, the detection and analysis of emotions assume paramount importance. However, this task is inherently challenging due to the subjectivity inherent in emotions, lacking a universal consensus for quantification or categorization.OBJECTIVES: This research proposes a novel speech recognition model for emotion analysis, leveraging diverse machine learning techniques along with a three-layer feature extraction approach. This research will also through light on the robustness of models on balanced and imbalanced datasets. METHODS: The proposed three-layered feature extractor uses chroma, MFCC, and Mel method, and passes these features to classifiers like K-Nearest Neighbour, Gradient Boosting, Multi-Layer Perceptron, and Random Forest.RESULTS: Among the classifiers in the framework, Multi-Layer Perceptron (MLP) emerges as the top-performing model, showcasing remarkable accuracies of 99.64%, 99.43%, and 99.31% in the Balanced TESS Dataset, Imbalanced TESS (Half) Dataset, and Imbalanced TESS (Quarter) Dataset, respectively. K-Nearest Neighbour (KNN) follows closely as the second-best classifier, surpassing MLP's accuracy only in the Imbalanced TESS (Half) Dataset at 99.52%.CONCLUSION: This research contributes valuable insights into effective emotion recognition through speech, shedding light on the nuances of classification in imbalanced datasets.

Enhanced Classification of Imbalanced Medical Datasets using Hybrid Data-Level, Cost-Sensitive and Ensemble Methods

Addressing the class imbalance in classification problems is particularly challenging, especially in the context of medical datasets where misclassifying minority class samples can have significant repercussions. This study is dedicated to mitigating class imbalance in medical datasets by employing a hybrid approach that combines data-level, cost-sensitive, and ensemble methods. Through an assessment of the performance, measured by AUC-ROC values, Sensitivity, F1-Score, and G-Mean of 20 data-level and four cost-sensitive models on seventeen medical datasets - 12 small and five large, a hybridized model, SMOTE-RF-CS-LR has been devised. This model integrates the Synthetic Minority Oversampling Technique (SMOTE), the ensemble classifier Random Forest (RF), and the Cost-Sensitive Logistic Regression (CS-LR). Upon testing the hybridized model on diverse imbalanced ratios, it demonstrated remarkable performance, achieving outstanding performance values on the majority of the datasets. Further examination of the model's training duration and time complexity revealed its efficiency, taking less than a second to train on each small dataset. Consequently, the proposed hybridized model not only proves to be time-efficient but also exhibits robust capabilities in handling class imbalance, yielding outstanding classification results in the context of medical datasets.

Evolutionary simultaneous under and oversampling of instances for dealing with class-imbalance datasets in multilabel problems

Multilabel classification has recently attracted great attention from the data mining research community. Multilabel classification is concerned with learning where each instance can be associated with multiple classes (or labels). Class-imbalance problems appear in any classification task when the class distribution of the instances is very different. In multilabel classification, this problem is ubiquitous, as a large percentage of labels suffer from a class-imbalanced distribution. The adaptation of single-label methods to deal with the class-imbalance problem in multilabel learning is problematic as many of their basic concepts are not easily transferred. In this paper, we propose the use of evolutionary computation to simultaneously oversample the minority class and undersample the majority class for multilabel problems. Letting the algorithm autonomously select the instances to undersample and oversample allows us to extend these two successful paradigms to the multilabel task. An extensive comparison setup of 35 datasets shows the advantages of using this approach to deal with class-imbalance datasets for multilabel problems compared with previously published methods as well as the basic classification algorithms with the original datasets.

OBMI: oversampling borderline minority instances by a two-stage Tomek link-finding procedure for class imbalance problem

AbstractMitigating the impact of class imbalance datasets on classifiers poses a challenge to the machine learning community. Conventional classifiers do not perform well as they are habitually biased toward the majority class. Among existing solutions, the synthetic minority oversampling technique (SMOTE) has shown great potential, aiming to improve the dataset rather than the classifier. However, SMOTE still needs improvement because of its equal oversampling to each minority instance. Based on the consensus that instances far from the borderline contribute less to classification, a refined method for oversampling borderline minority instances (OBMI) is proposed in this paper using a two-stage Tomek link-finding procedure. In the oversampling stage, the pairs of between-class instances nearest to each other are first found to form Tomek links. Then, these minority instances in Tomek links are extracted as base instances. Finally, new minority instances are generated, each of which is linearly interpolated between a base instance and one minority neighbor of the base instance. To address the overlap caused by oversampling, in the cleaning stage, Tomek links are employed again to remove the borderline instances from both classes. The OBMI is compared with ten baseline methods on 17 benchmark datasets. The results show that it performs better on most of the selected datasets in terms of the F1-score and G-mean. Statistical analysis also indicates its higher-level Friedman ranking.

Handling class imbalance and overlap with a Hesitation-based instance selection method

Class imbalance is a common problem in machine learning, particularly in classification tasks. When the distribution of instances across known classes is biased or skewed, this issue leads to poor predictive performance. This is especially true for the minority class, which is often of greater importance in problems. However, class imbalance is not the only factor that can decrease performance. Overlapping problems and borderline instances can also degrade classification performance. Conventional imbalanced learning methods often balance the distribution between classes, for example, by oversampling the minority class or under sampling the majority class. However, these methods may not adequately address the difficulties caused by overlapping and borderline instances. The present paper expands on the classification of imbalanced datasets by addressing the issue of how boundary instances should be sampled to handle class imbalance and control class overlap. The designed Hesitation degree-based instance weighting method can identify the impact of instances on classifier performance while reducing the skewness of the dataset and alleviating the possibility of class overlap. Additionally, by integrating a chaotic evolutionary algorithm with the designed classifier, the most important instances can be selected. Statistical results show that the proposed method outperforms state-of-the-art methods in terms of reduction rate, error rate, and G-mean.

Effects of Preprocessing on Text Classification in Balanced and Imbalanced Datasets

Effects of Preprocessing on Text Classification in Balanced and Imbalanced Datasets

A post-processing framework for class-imbalanced learning in a transductive setting

Traditional classification tasks suffer from the class-imbalanced problem, where some classes far outnumber others. To address this issue, existing class-imbalanced learning (CIL) methods either preprocess class-imbalanced datasets or adapt traditional classification algorithms to the imbalanced class distribution. Inspired by the idea of transductive learning, we propose a post-processing framework called PPF for CIL. Distinct from existing CIL methods, PPF directly adjusts the predicted labels of test data to fit the imbalanced class distribution. Specifically, we relabel some test data according to their prediction probabilities so that the class proportion of test data is close to that of training data. The underlying assumption is that training and test data, drawn independently from one data space, should obey the same class distribution. Furthermore, we propose a Compact Prototype-based Nearest Neighbor (CPNN) algorithm to assist the original classifier with the adjustment. Instead of training a classifier, CPNN classifies test data according to their distances to a set of prototypes estimated on labeled data. Thus, it is computationally simple and relatively robust to class imbalance. As a general framework, PPF can be easily applied to both traditional classification and CIL algorithms. To validate the effectiveness of the proposed method, we conducted extensive experiments on a variety of class-imbalanced datasets, using SVM and C4.5 as the original classifiers, respectively. Measured by F-measure, G-mean, and AUC, both PPF-SVM and PPF-C4.5 outperform 10 state-of-the-art CIL algorithms. Additionally, PPF further improved their performances when applied to 10 CIL algorithms.

Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning

BackgroundOral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific morbidity and mortality. Recently, there have been studies to apply the rapidly advancing technology of deep learning for diagnosing oral cavity cancer and OPMDs. However, several challenging issues such as class imbalance must be resolved to effectively train a deep learning model for medical imaging classification tasks. The aim of this study is to evaluate a new technique of artificial intelligence to improve the classification performance in an imbalanced tongue lesion dataset.MethodsA total of 1,810 tongue images were used for the classification. The class-imbalanced dataset consisted of 372 instances of cancer, 141 instances of OPMDs, and 1,297 instances of noncancerous lesions. The EfficientNet model was used as the feature extraction model for classification. Mosaic data augmentation, soft labeling, and curriculum learning (CL) were employed to improve the classification performance of the convolutional neural network.ResultsUtilizing a mosaic-augmented dataset in conjunction with CL, the final model achieved an accuracy rate of 0.9444, surpassing conventional oversampling and weight balancing methods. The relative precision improvement rate for the minority class OPMD was 21.2%, while the relative F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} score improvement rate of OPMD was 4.9%.ConclusionsThe present study demonstrates that the integration of mosaic-based soft labeling and curriculum learning improves the classification performance of tongue lesions compared to previous methods, establishing a foundation for future research on effectively learning from imbalanced data.

Impact of Feature Extraction Combined with Data Sampling Methods on Heartbeat Categorization

Dealing with class-imbalanced datasets in data analytics poses challenges, especially when faced with high-dimensional data. In order to handle this issue, researchers often utilize preprocessed methods like feature selection. Feature selection attempts to create a more informative and condensed feature set, while data sampling helps alleviate class imbalance. In our study, aim is to explore the effectiveness of data sampling preprocessed techniques combined with feature extraction using a dataset on ECG Heartbeat. We evaluate ensemble classifiers: Decision Tree; Random Forests (RF), Gradient-Boosted Trees (GBT) for feature extraction. In terms of data sampling, we assess the effectiveness of two methods: Random Under sampling (RUS) and Synthetic Minority Oversampling (SMOTE). The performance of this feature extraction is measured using the sensitivity and the specificity, two important metrics used for accuracy. Our findings depict that the combination of the RUS and GBT method yields the highest performance for ECG Heartbeat detection.

A Novel and Effective Multi-Class Classification Method for Imbalanced Medical Transcriptions

Natural Language Processing (NLP) techniques are used to glean information from Electronic Health Records (EHR) for identifying patients with unique clinical characteristics and defining phenotypes. The classification of imbalanced datasets is also one of the vital concerns in medical diagnosis. We built an improved framework for automating the multi-class classification of imbalanced medical transcriptions [1] into 40 medical specialties, by creating a set of important phenotypes/features. We implemented and tested five machine learning models out of which Random Forest Classifier has achieved the highest performance of 0.99 F1 score (precision 0.99, recall 0.99) and roc-auc-score of 0.99 on test data.

Feature selection and its combination with data over-sampling for multi-class imbalanced datasets

Feature selection aims at filtering out some unrepresentative features from a given dataset in order to construct more effective learning models. Furthermore, ensemble feature selection by combining multiple feature selection methods has shown its outperformance over single feature selection. However, the performances of different (ensemble) feature selection methods have not been fully examined over multi-class imbalanced datasets. On the other hand, for class imbalanced datasets, one widely considered solution is to re-balance the datasets by data over-sampling, which generates some synthetic examples for the minority classes. However, the effect of performing (ensemble) feature selection on over-sampling multi-class imbalanced datasets has not been investigated. Therefore, the first research objective is to examine the performances of single and ensemble feature selection methods by fifteen well-known filter, wrapper, and embedded algorithms in terms of classification accuracy. For the second research objective, two orders of combining the feature selection and over-sampling steps are compared in order to find out the best combination procedure as well as the best combined algorithms. The experimental results based on ten different domain datasets containing low to very high feature dimensions show that ensemble feature selection methods slightly perform better than single ones. However, their performance differences are not big. To combine with the Synthetic Minority Oversampling Technique (SMOTE) over-sampling algorithm, performing feature selection first and over-sampling second outperforms the other procedure. Although the best combined algorithms are based on ensemble feature selection, eXtreme Gradient Boosting (XGBoost), as the single best feature selection algorithm, combined with SMOTE provides very similar classification performance to the best combined algorithms. To consider the issues of classification performance and compactional cost, the optimal solution is based on the combined XGBoost and SMOTE.

Classifier Ensemble Based on Multiview Optimization for High-Dimensional Imbalanced Data Classification.

High-dimensional class imbalanced data have plagued the performance of classification algorithms seriously. Because of a large number of redundant/invalid features and the class imbalanced issue, it is difficult to construct an optimal classifier for high-dimensional imbalanced data. Classifier ensemble has attracted intensive attention since it can achieve better performance than an individual classifier. In this work, we propose a multiview optimization (MVO) to learn more effective and robust features from high-dimensional imbalanced data, based on which an accurate and robust ensemble system is designed. Specifically, an optimized subview generation (OSG) in MVO is first proposed to generate multiple optimized subviews from different scenarios, which can strengthen the classification ability of features and increase the diversity of ensemble members simultaneously. Second, a new evaluation criterion that considers the distribution of data in each optimized subview is developed based on which a selective ensemble of optimized subviews (SEOS) is designed to perform the subview selective ensemble. Finally, an oversampling approach is executed on the optimized view to obtain a new class rebalanced subset for the classifier. Experimental results on 25 high-dimensional class imbalanced datasets indicate that the proposed method outperforms other mainstream classifier ensemble methods.

Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study

This study explores the efficacy of a Voting Classifier, combining Logistic Regression, Random Forest, and Gaussian Naive Bayes, in the classification of neurodegenerative diseases, focusing on Alzheimer's Disease (AD), Parkinson’s Disease (PD), and control groups. Utilizing a dataset pre-processed with Canny segmentation and Hu Moments feature extraction, the research aimed to address the challenges posed by imbalanced datasets in medical image classification. The classifier's performance was evaluated through a 5-fold cross-validation approach, with metrics including accuracy, precision, recall, and F1-Score. The results revealed a consistent recall rate of approximately 46% across all folds, indicating the model's effectiveness in identifying cases of neurodegenerative diseases. However, the precision and F1-Score were notably lower, averaging around 22% and 29%, respectively, underscoring the difficulties in achieving accurate classification in imbalanced datasets. The study contributes to the understanding of machine learning applications in medical diagnostics, specifically in the challenging context of neurodegenerative disease classification. It highlights the potential of using advanced image processing techniques combined with machine learning ensembles in enhancing diagnostic accuracy. However, it also draws attention to the inherent challenges in such approaches, particularly regarding precision in imbalanced datasets. Recommendations for future research include exploring data balancing techniques, alternative feature extraction methods, and different machine learning algorithms to improve the precision and overall performance. Additionally, applying the model to a broader and more diverse dataset could provide more generalizable and robust findings. This study is significant for researchers and practitioners in medical imaging and machine learning, offering insights into the complexities and potential of automated disease classification

Irrelevant attribute resistance approach to binary classification for imbalanced data

Imbalanced data distribution is a common feature in real-world datasets. For imbalanced data, the imbalanced characteristics of the classes have two negative effects on classification results, one of which is that the minority class is highly likely masked by the majority class so as to weaken the ability of the classifiers to identify the minority class. Another effect is that irrelevant attributes hidden in imbalanced data can create much noise to interfere the classifiers, thereby leading to that the classifiers could mistakenly treat noise as the minority classes. In this scenario, the performance of the classifiers is rapidly declined and the classifiers obtain incorrect classification results. To address this issue, this paper proposed a conformal transformation twin-hypersphere with fuzzy. The critical thought is that using conformal transformation to explore the regions containing minority classes, by so doing, minority classes can be more likely to be noticed by the classifier. Using the proposed fuzzy function assesses the contributions of points to the hypersphere training, through evaluating the contributions, noise can be determined, thereby increasing the ability of the classifier to noise resistance. Results on the synthetic and real datasets show that the proposed method outperforms the competitors in classification accuracy and noise resistance. Results also imply that the proposed method does not exhibit exponential calculation time, meaning that the method is suitable for the classification of large-scale imbalanced datasets. We demonstrate that conformal transformation can assist those non-linear kernels to find those hard-to-observe regions containing minority classes, thereby strengthening the adaptability of the classifiers to imbalanced data following complex distributions. Moreover, the non-linear kernels using conformal transformation can adapt to the situation where different sub-regions in sample space require different nonlinearities.

Enhancing the Classification of Imbalanced Datasets through the Utilization of the Provisional Clarifier-Alternator (cPAen)

Warmcomfort data is vital for enhancing heating and cooling system efficiency through ML (ML) models. Yet, these datasets often suffer from severe class imbalance due to subjective feedback. To tackle this issue, we introduce a data augmentation algorithm, Provisional Clarifier-Alternator (cPAen). In our research, we evaluated cPAen using real Warmcomfort data and found it outperformed SMOTE, ADASYN, and cWGAN-GP in terms of F1 scores. Notably, cPAen is over 5 times faster than cWGAN-GP while maintaining high test accuracy.

3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data

Three-dimensional Mineral Prospectivity Mapping (3DMPM) is an innovative approach to mineral exploration that combines multiple geological data sources to create a three-dimensional (3D) model of a mineral deposit. It provides an accurate representation of the subsurface that can be used to identify areas with mineral potential. These 3D geological models are the typical data source for 3D prospective modeling. Geological data sets from multiple sources are used to construct 3D geological models. Since in practice there is a significant imbalance in the ratio of mineralized to non-mineralized classes, the classification results will be biased in favor of the more observed classes. Borderline-SMOTE (BLSMOTE) is an oversampling technique used to solve the problem of unbalanced datasets and works by generating synthetic data points along the boundary line between the minority and majority classes. This helps to create a more balanced dataset without introducing too much noise. Non-mineralized samples can be generated by randomly selecting non-mineralized locations, which means that uncertainties are generated. In this paper, we take the shallow-forming low-temperature hydrothermal deposit Guizhou Lannigou gold deposit as an example to extract the ore-controlling elements and establish a 3D geological model. A total of 50 training samples are generated using the sampling method described above, and 50 mineralization prospects are generated using Random Forests. A return–risk analysis was used to explore the uncertainties associated with synthetic positive samples and randomly selected negative samples, and to determine the final mineral potential values. Based on the evaluation metrics G-mean and F-value, the model using BLSMOTE outperforms the model without the synthetic algorithm and the models using SMOTE and KMeansSMOTE. The optimal model BLSMOTE18 has an AUC of 0.9288. The methodology also performs superiorly with different levels of class imbalance datasets. Excluding the predictions where the results highly overlap with known deposits, five target zones were circled for the targets using a P-A plot, all of which have obvious metallogenic geological features. Among them, Target1 and Target2 have good potential for mineralization, which is of great significance for future mineral exploration work.