Optimizing crop type mapping for fairness 

Objective.Accurate localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery, but the class imbalance of epileptogenic vs. non-epileptogenic electrode contacts in intracranial electroencephalography (iEEG) data poses significant challenges for automatic localization methods. This review evaluates methodologies for handling the class imbalance in EZ localization studies that use machine learning (ML).Approach.We systematically reviewed studies employing ML to localize the EZ from iEEG data, focusing on strategies for addressing class imbalance in data handling, algorithm design, and evaluation.Results.Out of 2,128 screened studies, 35 fulfilled the inclusion criteria. Across the studies, the iEEG contacts annotated as epileptogenic prior to automatic localization constituted a median of 18.34% of all contacts. However, many of these studies did not adequately address the class imbalance problem. Techniques such as data resampling and cost-sensitive learning were used to mitigate the class imbalance problem, but the chosen evaluation metrics often failed to account for it.Significance.Class imbalance significantly impacts the reliability of EZ localization models. More comprehensive management and innovative approaches are needed to enhance the robustness and clinical utility of these models. Addressing class imbalance in ML models for EZ localization will improve both the predictive performance and reliability of these models.

One of the factors that affect the performance of the classification predictive modelling task is the skewed distribution of the classes in the training data, which is known as the “class imbalance problem”. It hinders the performance of the predictive models. The contribution of this paper is threefold; primarily, this paper discusses the latest techniques used in different domains to handle class imbalance. Then, the taxonomy of data preprocessing, algorithm level and hybrid techniques have been presented. Finally, various evaluation strategies and application domains are presented. This paper covers all the latest and most effective techniques and elaborates on effective techniques such as cost-sensitive learning and ensemble learning, which collectively provide the reader with an extensive overview of the techniques used by researchers to alleviate skewed class distribution of the classes in the training data.KeywordsClass imbalance problemClassification predictive modellingData preprocessingCost-sensitive learningOne-class learningEnsemble learningHybrid learning techniquesEvaluation metrics

Class imbalance and noisy data widely exist in real-world problems, and the support vector machine (SVM) is hard to construct good classifiers on these data. Fuzzy SVMs (FSVMs), as variants of SVM, use a fuzzy membership function both to reflect the samples’ importance and to remove the impact of noises, and employ cost-sensitive technology to address the class imbalance. They can handle the noise and class imbalance problems in many cases; however, the fuzzy membership functions are often affected by the class imbalance data, leading to inaccurate measures for samples’ performance and affecting the performance of FSVMs. To solve this problem, we design a new fuzzy membership function and combine it with cost-sensitive learning to deal with the class imbalance problem with noisy data, named Slack-Factor-based FSVM (SFFSVM). In SFFSVM, the relative distances between samples and an estimated hyperplane, called slack factors, are used to define the fuzzy membership function. To eliminate the impact of class imbalance on the function and gain more accurate samples’ importance, we rectify the importance according to the positional relationship between the estimated hyperplane and the optimal hyperplane of the problem, and the slack factors of samples. Comprehensive experiments on artificial and real-world datasets demonstrate that SFFSVM outperforms other comparative methods on F1, MCC, and AUC-PR metrics.

Crop type identification and mapping have been a classic issue essential for agriculture characterisation and food security as well as crop health monitoring. Remote sensing was proven to be an efficient tool to define and study agriculture. The complementarity between optical and radar data can hugely improve crop type identification and mapping. This document presents an overview of more than 40 recent works between 2013 and 2019, related to crop type mapping using satellite imagery, and points the general techniques used. Different classification algorithms was studied and compared, as well as the comparison of the inputs, optimal bands and polarization for radar data, and process giving the best results. It is often concluded that fusion improves results when compared with single data using. Nevertheless, it had been challenging to combine datasets from sensors that had different physical concepts, and progress in this domain require the development of systematic procedures.

ADANOISE: Training neural networks with adaptive noise for imbalanced data classification

The class imbalance problem is a well-known classification challenge in machine learning that has vexed researchers for over a decade. Under-representation of one or more of the target classes (minority class(es)) as compared to others (majority class(es)) can restrict the application of conventional classifiers directly on the data. In addition, emerging challenges such as overlapping classes, make class imbalance even harder to solve. Class overlap is caused due to ambiguous regions in the data where the prior probability of two or more classes are approximately equal. We are motivated to address the challenge of class overlap in the presence of imbalanced classes by a problem in pervasive computing. Specifically, we are designing smart environments that perform health monitoring and assistance. Our solution, ClusBUS, is a clustering-based under sampling technique that identifies data regions where minority class samples are embedded deep inside majority class. By removing majority class samples from these regions, ClusBUS preprocesses the data in order to give more importance to the minority class during classification. Experiments show that ClusBUS achieves improved performance over an existing method for handling class imbalance.

In this paper, we propose to deal with the problems of logistic regression with outliers and class imbalance, which are common in a wide range of practical applications. The robust bounded logistic regression with different error costs is developed to reduce the combined influence of outliers and class imbalance. First, inspired by the Correntropy induced loss function, we develop the bounded logistic loss function which is a monotonic, bounded and nonconvex loss and thus robust to outliers. With the bounded logistic loss, we construct a new robust logistic regression. Second, under the principle of cost-sensitive learning, we assign different error costs for different classes in order to reduce the sensitiveness of the new robust logistic regression to class imbalance. Using the half-quadratic optimization method, it is easy to optimize the proposed logistic regression model. Experimental results demonstrate that our proposed method improves the performance of logistic regression on the datasets with outliers and class imbalance.

Having more samples belonging to one class than the samples of the other class in data used in a classification task is known as class imbalance problem. Handling class imbalance is crucial since the classifier's performance is highly affected. One of the solution approaches of this problem is to make the data balanced by generating synthetic data. Employing resampling methods is a common way of generating synthetic data. Although adversarial generative networks (GANs) are mainly designed to generate image data, they can also be an alternative to solve the class imbalance problem by generating tabular data. This work presents a comparative study of resampling methods with GANs based methods. The performance of machine learning methods improved by 27% if the data is balanced with resampling methods. However, similar performance results were observed with working on imbalanced data if the GANs based methods are employed for synthetic data generation.

Automation in arrhythmia classification helps medical professionals to make accurate decisions upon the patient's health. Classification becomes complicated when class overlapping and class imbalance problem occurs together. The aim of this work is to improve the arrhythmia classification accuracy. Proposed methodology consists of fisher discriminant ratio based feature ranking stage and anomaly detection based training sample selection stage followed by classification using probabilistic neural network classifier. As per the recommendations of the Association for the Advancement of Medical Instrumentation, five arrhythmia classes were classified. The proposed method resulted in average sensitivity, positive predictive value and F Score of 95.37%, 98.35% and 96.72%, respectively. The experimental results revealed that: (1) Selected non-overlapping features were able to better discriminate arrhythmia classes, (2) Mixture of Gaussians based anomaly detection method suited well to handle the class imbalance problem and (3) Minority classes with few training samples were also correctly classified using the proposed method.

Class imbalance is a common problem in classification tasks. The learning schemes of most classification algorithms tend to optimize the overall accuracy, and thus, identification of important but rarely occurring examples is ignored. The Mahalanobis–Taguchi system (MTS) has been shown to be robust in addressing class imbalance problems owing to its inherent properties of classification model construction. The bagging learning approach often has been applied as a superior strategy to reduce the learning bias of classification algorithms. In this study, we propose MTSbag, which integrates the MTS and the bagging-based ensemble learning approaches to enhance the ability of conventional MTS in handling imbalanced data. We perform numerical experiments involving multiple datasets with various class imbalance levels to demonstrate the effectiveness of MTSbag, especially for datasets with high imbalance levels. Finally, as a healthcare application, an early warning system for in-hospital cardiac arrest, was successfully implemented by leveraging the minority class identification ability of MTSbag.

A self-adaptive weighted extreme learning machine (SawELM) is proposed in this paper to deal with the imbalanced binary-class classification problems. SawELM calculates the output-layer weights based on a newly-designed self-adaptive mechanism which includes the following two modules: one is to gradually reduce the weights of wrongly-classified training instances and the other is to dynamically update the outputs of these wrongly-classified instances. On 50 imbalanced binary-class data sets selected from KEEL repository, we compare the accuracy, G-mean, and F-measure of SawELM with unweighted ELM (UnWELM) and weighted ELM (WELM). The experimental results show that the newly-designed self-adaptive mechanism is effective and SawELM obviously improves the imbalanced classification performance of WELM. SawWLM obtains the significantly higher G-mean and F-measure than UnWELM and WELM. Meanwhile, the accuracy of SawELM is better than WELM and comparable to UnWELM.

Class imbalance is an important problem in machine learning. With increases in available information and the growing use of Big Data sources to extract meaning from data, the challenges associated with class imbalance continue to influence research and shape business value. In this paper, we focus on using highly imbalanced Big Data from Medicare to detect provider claims fraud. We combine three Medicare parts and generate fraud labels using real-world excluded providers. The number of known fraudulent providers is very small, with 0.062% of the combined dataset being labeled as fraud, indicating severe class imbalance. To address class imbalance concerns, we provide experimental results incorporating six different data sampling methods (undersampling and oversampling) to create datasets for five class ratios (imbalanced to balanced), as well as using the full dataset (with no sampling). Three state-of-the-art machine learning models with Apache Spark are used to assess Medicare fraud detection performance across data sampling methods and class ratios. We demonstrate that data sampling, in particular random undersampling, presents good results across all learners, whereas oversampling provides no benefit versus models built using the full dataset.

Class imbalance problems are prevalent in the real world. In such cases, traditional supervised algorithms tend to have difficulty in recognizing minority data because the models are likely to maximize prediction accuracy by simply ignoring the minority data. To address the class imbalance problem, various approaches have been tried, including data preprocessing techniques, cost-sensitive learning, and ensemble modeling. Recently, several hybrid models combining sampling methods with boosting have been proposed such as RUSBoost, LIUBoost, and CUSBoost. In this study, a novel under-sampling-based boosting method named MPSUBoost is proposed to handle the class imbalance problem. The proposed method is an integration of modified PSU and AdaBoost. The performance benchmark testing conducted on 35 highly imbalanced datasets indicated that the proposed method provided performance improvement over three existing methods (RUSBoost, LIUBoost, and CUSBoost). Moreover, we verified that the samples obtained by MPSUBoost effectively represented the given majority data, which led to a competitive advantage in the imbalanced data, particularly when true positives are imperative.

The class imbalance problem has been said recently to hinder the performance of learning systems. In fact, many of them are designed with the assumption of well-balance datasets. However, it is very common to find higher presence of one of the classes in real classification problems. The aim of this paper is to make a preliminary analysis on the effect of the class imbalance problem in learning classifier systems. Particularly we focus our study on UCS, a supervised version of XCS classifier system. We analyze UCS's behavior on unbalanced datasets and find that UCS is sensitive to high levels of class imbalance. We study strategies for dealing with class imbalances, acting either at the sampling level or at the classifier system's level.

KNN weighted reduced universum twin SVM for class imbalance learning