Handling Class Imbalance Problem Research Articles

Exploratory Analysis of Methods, Techniques, and Metrics to Handle Class Imbalance Problem

Class imbalance a common challenge in machine learning, often results in skewed predictions and misrepresentative model assessments, highlighting the need for effective countermeasures. Our detailed survey dives into three primary techniques for addressing this issue: data-level interventions, algorithmic modifications, and integrated hybrid solutions. We thoroughly dissect each approach, delineating its merits, drawbacks, and ideal use cases. Data-level methods aim to restructure the dataset for class balance, while algorithmic techniques recalibrate the learning process to better detect the minority class. The hybrid strategies merge the benefits of both for a holistic remedy. The study further emphasizes the importance of precise evaluation metrics, elaborating on both conventional metrics and those tailored for imbalanced scenarios. Our objective is to arm professionals with a deep insight into tackling class imbalance, especially within the big data framework. The insights shared aspire to inspire the creation of resilient, equitable machine learning models adapt at navigating imbalanced data, ensuring enhanced prediction fidelity and consistency.

SOM-US: A Novel Under-Sampling Technique for Handling Class Imbalance Problem

SOM-US: A Novel Under-Sampling Technique for Handling Class Imbalance Problem

An Innovative Approach for Predicting Software Defects by Handling Class Imbalance Problem

From last decade unbalanced data has gained attention as a major challenge for enhancing software quality and reliability. Due to evolution in advanced software development tools and processes, today’s developed software product is much larger and complicated in nature. The software business faces a major issue in maintaining software performance and efficiency as well as cost of handling software issues after deployment of software product. The effectiveness of defect prediction model has been hampered by unbalanced data in terms of data analysis, biased result, model accuracy and decision making. Predicting defects before they affect your software product is one way to cut costs required to maintain software quality. In this study we are proposing model using two level approach for class imbalance problem which will enhance accuracy of prediction model. In the first level, model will balance predictive class at data level by applying sampling method. Second level we will use Random Forest machine learning approach which will create strong classifier for software defect. Hence, we can enhance software defect prediction model accuracy by handling class imbalance issue at data and algorithm level.

Classification of Imbalanced Data Using SMOTE and AutoEncoder Based Deep Convolutional Neural Network

The imbalanced data classification is a challenging issue in many domains including medical intelligent diagnosis and fraudulent transaction analysis. The performance of the conventional classifier degrades due to the imbalanced class distribution of the training data set. Recently, machine learning and deep learning techniques are used for imbalanced data classification. Data preprocessing approaches are also suitable for handling class imbalance problem. Data augmentation is one of the preprocessing techniques used to handle skewed class distribution. Synthetic Minority Oversampling Technique (SMOTE) is a promising class balancing approach and it generates noise during the process of creation of synthetic samples. In this paper, AutoEncoder is used as a noise reduction technique and it reduces the noise generated by SMOTE. Further, Deep one-dimensional Convolutional Neural Network is used for classification. The performance of the proposed method is evaluated and compared with existing approaches using different metrics such as Precision, Recall, Accuracy, Area Under the Curve and Geometric Mean. Ten data sets with imbalance ratio ranging from 1.17 to 577.87 and data set size ranging from 303 to 284807 instances are used in the experiments. The different imbalanced data sets used are Heart-Disease, Mammography, Pima Indian diabetes, Adult, Oil-Spill, Phoneme, Creditcard, BankNoteAuthentication, Balance scale weight & distance database and Yeast data sets. The proposed method shows an accuracy of 96.1%, 96.5%, 87.7%, 87.3%, 95%, 92.4%, 98.4%, 86.1%, 94% and 95.9% respectively. The results suggest that this method outperforms other deep learning methods and machine learning methods with respect to G-mean and other performance metrics.

Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem

Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal with the problem, they have yet to consider how to select a proper treatment, especially when uncertainty levels are high. Applying rough-fuzzy theory to the imbalanced data learning problem could be a promising research direction that generates the synthetic data and removes the outliers. The proposed work identifies the positive, boundary, and negative regions of the target set using the rough set theory and removes the objects in the negative region as outliers. It also explores the positive and boundary regions of the rough set by applying the fuzzy theory to generate the samples of the minority class and remove the samples of the majority class. Thus the proposed rough-fuzzy approach performs both oversampling and undersampling to handle the imbalanced class problem. The experimental results demonstrate that the novel technique allows qualitative and quantitative data handling.

Data augmentation using conditional generative adversarial network (cGAN): Application for prediction of corrosion pit depth and testing using neural network

Machine learning (ML) based algorithms, due to their ability to model nonlinear and complex relationship, have been used in predicting corrosion pit depth in oil and gas pipelines. Class imbalance and data scarcity are the challenging problems while training ML models. This paper utilized a conditional generative adversarial network (cGAN) to handle class imbalance problem in a corrosion dataset by generating new samples. Utility of the cGAN data augmentation is evaluated by training an artificial neural network (ANN) model. In addition, random oversampling and Borderline-SMOTE data generating techniques are used for comparison with cGAN. The testing accuracy of the ANN model increased greatly when trained by the cGAN based augmented dataset and this model performance improvement can be useful for a pipeline integrity management.

A Survey of Different Approaches for the Class Imbalance Problem in Software Defect Prediction

The imbalanced nature of the software datasets leads to the biased learning of prediction model toward the observations of the majority class (non-defective class). The prediction model can produce poor results for the minority class observations. Such misappropriations can prove costly especially in software development where minority class (defective) is the one that has the highest interest from the learning point of view. Various approaches have been used for dealing with class imbalance problem of software defect prediction but no one dominates and hence developing a generalized software defect prediction model for imbalanced datasets remains problematic. This paper surveys existing approaches for handling class imbalance problem of software defect datasets. In this survey, most relevant software defect prediction studies and identified the two main approaches that have been used for handling imbalance issue of software defect datasets. Furthermore, we also provide some comparison of findings in state-of-the-art literature and the guidelines for carrying out future research.

10 Deep Learning for Multi-Behavioral Video Classification of Interactive Behaviors of Pigs in Single-Spaced Automatic Feeders

Abstract Automatic pig feeders record individual feeding behavior traits that can be used in genetic selection. However, these feeders cannot measure how feeding traits are affected by behavioral interactions between animals. Moreover, recording behavioral interactions can improve the estimation of social genetic effects. This motivated our assessment of computer vision to classify 4 types of interactions at the feeder: head-to-body contact (HB) including head knocking, tail biting, pushing, gentle nosing and casual contact between head/ears of a pig with a feeding pig; levering (L) where the feeding pig was lifted from behind by another pig; mounting (M) where the feeding pig was mounted by another pig; and no-contact (NC) when a second pig entered the feeder without touching the feeding pig. Behavior at the feeder was filmed twice, 3 weeks apart, for 2 consecutive days each week in 6 groups of grow-finish pigs (10 per group) housed in pens equipped with FIRE feeders. Video segments that involved 2 pigs in the feeder were selected, and labeled as HB (n=10,114), L (n=925), M (n=1,242) and NC (n=3,398). Due to the sparse data available for training and the complex nature of the behavioral interactions, we utilized pretrained convolutional neural networks for automatic spatial feature extraction followed by long short-term memory for temporal feature extraction from sequences of frames. Focal loss, a loss function that assigns different weights to hard/easily misclassified examples to handle class imbalance problem, was used in this study. Accuracy, recall, and precision of behavior classification were obtained from 5 random cross-validations. The overall accuracy was 0.967±0.002. The average recalls for HB, L, M, and NC were 0.977±0.004, 0.866±0.047, 0.969±0.011, and 0.964±0.009 respectively, while average precisions were 0.976±0.002, 0.901±0.029, 0.956±0.018, and 0.963±0.012. The proposed algorithm accurately classified multiple interactive behaviors in an automated feeding stall from digital videos.

A Novel Technique to Defraud Credit Card by Handling Class Imbalance Problem Using Machine Learning

Typically, classification algorithms work poorly when confronted with unbalanced datasets, and the resulting effects are skewed against the majority class. As a result, an effective model is required to identify unbalanced data, particularly in the context of fraud detection. For these types of issues, the classifier's accuracy is not trusted because the cost of predicting a fraud sample as a non-fraud sample is extremely high. In general, imbalanced learning happens when some types of data distributions significantly outnumber other data distributions in the instance space. There is a need of technique such as under sampling or oversampling in order to learn from unbalanced datasets. A novel over sampling method has been suggested for learning from unbalanced datasets in this paper. The basic impression here is that a weighted distribution for diverse outnumbered class instances has been utilized depending on their degree of complexity to learn, with more pretended evidence leading to the outnumbered ones, being more troublesome to learn. As a result, with regard to data distributions, the suggested approach improves learning first by bringing down the bias familiarized using class difference, and then by pliantly conveying the classification judgement boundary toward challenging instances.

Handling Class Imbalance in Online Transaction Fraud Detection

With the rise of internet facilities, a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction. However, the fraud cases have also increased causing the loss of money to the consumers. Hence, an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time. Generally, the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem. In this research work, an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e., fraud transactions. A novel loss function named Weighted Hard- Reduced Focal Loss (WH-RFL) has been proposed which has achieved maximum fraud detection rate i.e., True Positive Rate (TPR) at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate (TNR) in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional datasets. Also, Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.

Handling class Imbalance problem in Intrusion Detection System based on deep learning

Handling class Imbalance problem in Intrusion Detection System based on deep learning

Handling class imbalance problem in software maintainability prediction: an empirical investigation

Handling class imbalance problem in software maintainability prediction: an empirical investigation

Hybrid approach redefinition with cluster-based instance selection in handling class imbalance problem

Class Imbalance problems often occur in the classification process, the existence of these problems is characterized by the tendency of a class to have instances that are much larger than other classes. This problem certainly causes a tendency towards low accuracy in minority classes with smaller number of instances and also causes important information on minority classes not to be obtained. Various methods have been applied to overcome the problem of the imbalance class. One of them is the Hybrid Approach Redefinition method which is one of the Hybrid Ensembles methods. The tendency to pay attention to the performance classifier, has led to an understanding of the importance of selecting an instance that will be used as a classifier. In the classic Hybrid Approach Redefinition method classifier selection is done randomly using the Random Under Sampling approach, and it is interesting to study how performance is obtained if the sampling process is based on Cluster-Based by selecting existing instances. The purpose of this study is to apply the Hybrid Approach Redefinition method with Cluster-Based Instance Selection (CBIS) approach so that it can obtain a better performance classifier. The results showed that Hybrid Approach Redefinition with cluster-based instance selection gave better results on the number of classifiers, data diversity, and performance classifiers compared to classic Hybrid Approach Redefinition.

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples.

BackgroundMachine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments. Hence, machine learning techniques are widely used in cancer detection and prognosis.MethodsIn this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets to generate sub-datasets, each of which contains a subset of the most relevant/informative attributes of the original dataset. The MFSAC method is a feature selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets.ResultsTo assess the performance of the proposed MFSAC-EC model, it is applied on different high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing models to establish its effectiveness with respect to other models. From the experimental results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes.

Developing a stroke alert trigger for clinical decision support at emergency triage using machine learning

BackgroundAcute stroke is an urgent medical condition that requires immediate assessment and treatment. Prompt identification of patients with suspected stroke at emergency department (ED) triage followed by timely activation of code stroke systems is the key to successful management of stroke. While false negative detection of stroke may prevent patients from receiving optimal treatment, excessive false positive alarms will substantially burden stroke neurologists. This study aimed to develop a stroke-alert trigger to identify patients with suspected stroke at ED triage. MethodsPatients who arrived at the ED within 12 h of symptom onset and were suspected of a stroke or transient ischemic attack or triaged with a stroke-related symptom were included. Clinical features at ED triage were collected, including the presenting complaint, triage level, self-reported medical history (hypertension, diabetes, hyperlipidemia, heart disease, and prior stroke), vital signs, and presence of atrial fibrillation. Three rule-based algorithms, ie, Face Arm Speech Test (FAST) and two flavors of Balance, Eyes, FAST (BE-FAST), and six machine learning (ML) techniques with various resampling methods were used to build classifiers for identification of patients with suspected stroke. Logistic regression (LR) was used to find important features. ResultsThe study population consisted of 1361 patients. The values of area under the precision-recall curve (AUPRC) were 0.737, 0.710, and 0.562 for the FAST, BE-FAST-1, and BE-FAST-2 models, respectively. The values of AUPRC for the top three ML models were 0.787 for classification and regression tree with undersampling, 0.783 for LR with synthetic minority oversampling technique (SMOTE), and 0.782 for LR with class weighting. Among the ML models, logistic regression and random forest models in general achieved higher values of AUPRC, in particular in those with class weighting or SMOTE to handle class imbalance problem. In addition to the presenting complaint and triage level, age, diastolic blood pressure, body temperature, and pulse rate, were also important features for developing a stroke-alert trigger. ConclusionsML techniques significantly improved the performance of prediction models for identification of patients with suspected stroke. Such ML models can be embedded in the electronic triage system for clinical decision support at ED triage.

Improved Hybrid Bag-Boost Ensemble With K-Means-SMOTE–ENN Technique for Handling Noisy Class Imbalanced Data

Abstract A class imbalance problem plays a vital role while dealing with classes with rare number of instances. Noisy class imbalanced datasets create considerable effect on the machine learning classification of classes. Data resampling techniques commonly used for handling class imbalance problem show insignificant behavior in noisy imbalanced datasets. To cure curse of data resampling technique in noisy class imbalanced data, we have proposed improved hybrid bag-boost with proposed resampling technique model. This model contains proposed resampling technique used for handling noisy imbalanced datasets. Proposed resampling technique comprises K-Means SMOTE (Synthetic Minority Oversampling TEchnique) as an oversampling technique and edited nearest neighbor (ENN) undersampling technique used as noise removal. This resampling technique is used to mitigate noise in imbalanced datasets at three levels, i.e. first clusters datasets using K-Means clustering technique, SMOTE inside clusters for handling imbalance by inducing synthetic instances of class in minority and lastly, using ENN technique to remove instances that create noise afterwards. Experiments were performed using 11 binary imbalanced datasets by varying attribute noise percentages, and by using area under receiver operating curve as performance metrics. Experimental results confirmed that proposed model shows better results than the rest. Moreover, it is also confirmed that proposed technique performs better with an increased noise percentage in binary imbalanced datasets.

Combining feature selection and hybrid approach redefinition in handling class imbalance and overlapping for multi-class imbalanced

<span>In the classification process that contains class imbalance problems. In addition to the uneven distribution of instances which causes poor performance, overlapping problems also cause performance degradation. This paper proposes a method that combining feature selection and hybrid approach redefinition (HAR) method in handling class imbalance and overlapping for multi-class imbalanced. HAR was a hybrid ensembles method in handling class imbalance problem. The main contribution of this work is to produce a new method that can overcome the problem of class imbalance and overlapping in the multi-class imbalance problem. This method must be able to give better results in terms of classifier performance and overlap degrees in multi-class problems. This is achieved by improving an ensemble learning algorithm and a preprocessing technique in HAR <span>using minimizing overlapping selection under SMOTE (MOSS). MOSS was known as a very popular feature selection method in handling overlapping. To validate the accuracy of the proposed method, this research use augmented R-Value, Mean AUC, Mean F-Measure, Mean G-Mean, and Mean Precision. The performance of the model is evaluated against the hybrid method (MBP+CGE) as a popular method in handling class imbalance and overlapping for multi-class imbalanced. It is found that the proposed method is superior when subjected to classifier performance as indicate with better Mean AUC, F-Measure, G-Mean, and precision.</span></span>

An Improved Hybrid Approach for Handling Class Imbalance Problem

Class imbalance issue that presents in many real-world datasets exhibit favouritism toward the majority class and showcases poor performance for the minority class. Such misclassifications may incur dubious outcome in case of disease diagnosis and other critical applications. Hence, it is a hot topic for the researchers to tackle the class imbalance issue. We present a novel hybrid approach for handling such datasets. We utilize simulated annealing algorithm for undersampling and apply support vector machine, decision tree, k-nearest neighbor and discriminant analysis for the classification task. We validate our technique in 51 real-world datasets and compare it with other recent works. Our technique yields better efficacy than the existing techniques and hence it can be applied in imbalance datasets to mitigate the misclassification.

A Multi-View Deep Neural Network Model for Chemical-Disease Relation Extraction From Imbalanced Datasets.

Understanding the chemical-disease relations (CDR) is a crucial task in various biomedical domains. Manual mining of these information from biomedical literature is costly and time-consuming. To address these issues, various researches have been carried out to design an efficient automatic tool. In this paper, we propose a multi-view based deep neural network model for CDR task. Typically, multiple representations (or views) of the datasets are not available for this task. So, we train multiple conceptually different deep neural network models on the dataset to generate different abstract features, treated as different views. A novel loss function, "Penalized LF", is defined to address the problem of imbalance dataset. The proposed loss function is generic in nature. The model is designed as a combination of Convolution Neural Network (CNN) and Bidirectional Long Short Term Memory (Bi-LSTM) network along with a Multi-Layer Perceptron (MLP). To show the efficacy of our proposed model, we have compared it with six baseline models and other state-of-the-art techniques, on "chemicals-and-disease-DFE" dataset, a free text dataset created by Li etal. from BioCreative V Chemical Disease Relation dataset. Results show that the proposed model attains highest F1-score for individual classes, proving its efficiency in handling class imbalance problem in the dataset. To further demonstrate the efficacy of the proposed model, we have presented results on BioCreative V dataset and two Protein-Protein Interaction Identification (PPI) datasets, viz., AiMed and BioInfer. All these results are also compared with the state-of-the-art models.

I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems

NIDSs identify malicious activities by analyzing network traffic. NIDSs are trained with the samples of benign and intrusive network traffic. Training samples belong to either majority or minority classes depending upon the number of available instances. Majority classes consist of abundant samples for the normal traffic as well as for recurrent intrusions. Whereas, minority classes include fewer samples for unknown events or infrequent intrusions. NIDSs trained on such imbalanced data tend to give biased predictions against minority attack classes, causing undetected or misclassified intrusions. Past research works handled this class imbalance problem using data-level approaches that either increase minority class samples or decrease majority class samples in the training data set. Although these data-level balancing approaches indirectly improve the performance of NIDSs, they do not address the underlying issue in NIDSs i.e. they are unable to identify attacks having limited training data only. This paper proposes an algorithm-level approach called I-SiamIDS, which is a two-layer ensemble for handling class imbalance problem. I-SiamIDS identifies both majority and minority classes at the algorithm-level without using any data-level balancing techniques. The first layer of I-SiamIDS uses an ensemble of b-XGBoost, Siamese-NN and DNN for hierarchical filtration of input samples to identify attacks. These attacks are then sent to the second layer of I-SiamIDS for classification into different attack classes using m-XGBoost. As compared to its counterparts, I-SiamIDS showed significant improvement in terms of Accuracy, Recall, Precision, F1-score and values of AUC for both NSL-KDD and CIDDS-001 datasets. To further strengthen the results, computational cost analysis was also performed to study the acceptability of the proposed I-SiamIDS.