Imbalanced Domains Research Articles

Synthetic to Realistic Imbalanced Domain Adaption for Urban Scene Perception

Deep neural networks (DNNs) technique has achieved impressive performance on semantic segmentation,while its training process requires a large amount of pixel-wise labelled data. Domain adaptation,as a promising solution,can break the restriction by training the model on synthetic data,and generalising it in real-world data. However,there is still a lack of attention paid to the imbalance problems on semantic segmentation adaptation,including the imbalance problem between i) source and target data,ii) different classes. To solve these problems,a progressive hierarchical feature alignment method is proposed in this paper. To alleviate the data imbalance problem,the network is progressively trained by the data from multisource domains,so as to obtain domain-invariant features. To address the class imbalance problem,the features are aligned hierarchically across domains. According to the experimental results,our method shows the competitive adapted segmentation performance on three benchmark datasets.

Machine Learning with Blind Imbalanced Domains

Recently machine learning is used in various applications and has shown success. Machine learning is good at learning the overall characteristics of massive training data. However, for real-world applications, training data often include multiple domains, and some domains have higher importance or risks. In this paper, we first propose a new problem setting: machine learning with blind imbalanced domains. In the proposed problem, the domain assignment of samples is unknown and imbalanced in the training data, and the performance is evaluated for each domain in the test data. Second, we propose an approach for that problem in classification tasks. The proposed approach combines center loss and weighted mini-batch sampling based on distances between samples and centroids in the deep feature space. Experiments on one minor domain and two minor domain settings using three handwritten digit databases (MNIST, EMNIST, and USPS) show that our proposed approach outperforms possible solutions using related methods. Remarkably our approach improves the accuracy in the minor domain by more than 1% on average. Furthermore, it can be inductively estimated that our proposed approach works on multiple domains given the successful results on one and two minor domains.

Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy.

Aim: In neuroscience research, data are quite often characterized by an imbalanced distribution between the majority and minority classes, an issue that can limit or even worsen the prediction performance of machine learning methods. Different resampling procedures have been developed to face this problem and a lot of work has been done in comparing their effectiveness in different scenarios. Notably, the robustness of such techniques has been tested among a wide variety of different datasets, without considering the performance of each specific dataset. In this study, we compare the performances of different resampling procedures for the imbalanced domain in stereo-electroencephalography (SEEG) recordings of the patients with focal epilepsies who underwent surgery.Methods: We considered data obtained by network analysis of interictal SEEG recorded from 10 patients with drug-resistant focal epilepsies, for a supervised classification problem aimed at distinguishing between the epileptogenic and non-epileptogenic brain regions in interictal conditions. We investigated the effectiveness of five oversampling and five undersampling procedures, using 10 different machine learning classifiers. Moreover, six specific ensemble methods for the imbalanced domain were also tested. To compare the performances, Area under the ROC curve (AUC), F-measure, Geometric Mean, and Balanced Accuracy were considered.Results: Both the resampling procedures showed improved performances with respect to the original dataset. The oversampling procedure was found to be more sensitive to the type of classification method employed, with Adaptive Synthetic Sampling (ADASYN) exhibiting the best performances. All the undersampling approaches were more robust than the oversampling among the different classifiers, with Random Undersampling (RUS) exhibiting the best performance despite being the simplest and most basic classification method.Conclusions: The application of machine learning techniques that take into consideration the balance of features by resampling is beneficial and leads to more accurate localization of the epileptogenic zone from interictal periods. In addition, our results highlight the importance of the type of classification method that must be used together with the resampling to maximize the benefit to the outcome.

No Free Lunch in imbalanced learning

The No Free Lunch (NFL) theorems have sparked intense debate since their publication, from theoretical and practical perspectives. However, to this date, no discussion is provided concerning its impact in the established field of imbalanced domain learning (IDL), known for its challenges regarding learning and evaluation processes. Most importantly, understanding the effect of commonly used solutions in such a field would prove very useful for future research. In this paper, we study the impact of data pre-processing methods, also known as resampling strategies, under the framework of the NFL theorems. Focusing on binary classification tasks, we claim that in IDL settings, when given a learning algorithm and a uniformly distributed set of target functions, the core conclusions of the NFL theorems are extensible to resampling strategies. As such, given no a priori knowledge or assumptions concerning data domains, any two resampling strategies are identical concerning their impact in the performance of predictive models. We provide a theoretical analysis and discussion on the intersection between IDL and the NFL theorems to support such a claim. Also, we collect empirical evidence via a thorough experimental study, including 98 data sets from multiple real-world knowledge domains.

A novel cost‐sensitive algorithm and new evaluation strategies for regression in imbalanced domains

AbstractMany real‐world data mining applications involve obtaining predictive models using imbalanced datasets. Frequently, the least common target variables present within datasets are associated with events that are highly relevant for end users. When these variables are nominal, we have a class‐imbalance problem which has been thoroughly studied within machine learning. As for regression tasks where target variables are continuous, few predictive models and evaluation techniques exist. This paper proposes a solution to these challenges. First, we introduce a cost‐sensitive learning algorithm based on a neural network trained on the minimization of a biased loss function. Results show a higher or comparable performance and convergence speed to existent techniques. Second, we develop new approaches for performance assessment of regression tasks within imbalanced domains by proposing new scalar measures, namely Geometric Mean Error (GME) and Class‐Weighted Error (CWE), as well as new graphical‐based measures, namely RECTPR, RECTNR, RECG − Mean and RECCWA curves. Unlike standard measures, our evaluation strategies are shown to be more robust to data imbalance as they reflect the performance of both rare and frequent events.

Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation.

Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly focus on practical knowledge transfer through domain alignment. Unfortunately, they ignore the fairness issue when the auxiliary source is extremely imbalanced across different categories, which results in severe under-presented knowledge adaptation of minority source set. To this end, we propose a Towards Fair Knowledge Transfer (TFKT) framework to handle the fairness challenge in imbalanced cross-domain learning. Specifically, a novel cross-domain knowledge propagation technique is proposed with the guidance of within-source and cross-domain structure graphs to smooth the manifold of the minority source set. Besides, a cross-domain fulfillment augmentation strategy is exploited achieve domain adaptation. Moreover, hybrid distinct classifiers and cross-domain prototype alignment are adopted to seek a more robust classifier boundary and mitigate the domain shift. Such three strategies are formulated into a unified framework to address the fairness issue and domain shift challenge. Extensive experiments over two popular benchmarks have verified the effectiveness of our proposed model by comparing to existing state-of-the-art DA models, and especially our model significantly improves over 20% on two benchmarks in terms of the overall accuracy.

Itinerant ferromagnetism in the repulsive Hubbard chain with spin-anisotropic odd-wave attraction

The ground-state properties of the Hubbard chain with on-site repulsion and anisotropic nearest-neighbor attraction are investigated by means of density matrix renormalization group calculations. The non-local attraction acts between fermions of one spin component only, mimicking the effect of p-wave Feshbach resonances in cold-atom systems. We analyze the onset of itinerant ferromagnetism, pinpointing the critical attraction strength where partially and fully ferromagnetic states occur. In the cold-atom setup, where the two (pseudo) spin populations are separately conserved, ferromagnetism occurs with the nucleation of a fully imbalanced band-insulating domain hosting the attractive component only. The size of this domain grows with the attraction strength, therefore increasing the (opposite) imbalance of the other domain, until the two spin components are fully separated. In the presence of a harmonic trap, the ferromagnetic state hosts a partially imbalanced domain in the center with an excess of the attractive component and filling lower than one. This central region is surrounded by fully imbalanced domains, located in the trap tails, hosting only fermions belonging to the other component.

Imbalanced regression and extreme value prediction

Research in imbalanced domain learning has almost exclusively focused on solving classification tasks for accurate prediction of cases labelled with a rare class. Approaches for addressing such problems in regression tasks are still scarce due to two main factors. First, standard regression tasks assume each domain value as equally important. Second, standard evaluation metrics focus on assessing the performance of models on the most common values of data distributions. In this paper, we present an approach to tackle imbalanced regression tasks where the objective is to predict extreme (rare) values. We propose an approach to formalise such tasks and to optimise/evaluate predictive models, overcoming the factors mentioned and issues in related work. We present an automatic and non-parametric method to obtain relevance functions, building on the concept of relevance as the mapping of target values into non-uniform domain preferences. Then, we propose SERA, a new evaluation metric capable of assessing the effectiveness and of optimising models towards the prediction of extreme values while penalising severe model bias. An experimental study demonstrates how SERA provides valid and useful insights into the performance of models in imbalanced regression tasks.

Object detection based on semi-supervised domain adaptation for imbalanced domain resources

On specified scenarios, models trained on specific datasets (source domain) can generalize well to novel scenes (target domain) via knowledge transfer. However, these source detectors might not be perfectly aligned with a low target resource due to the imbalanced and inconsistent domain shift involved. In this paper, we propose a semi-supervised detector that adapts the domain shifts on both appearance and semantic levels. Based on this, two components are introduced as appearance adaptation networks with instance and batch normalization, and semantic adaptation networks where an adversarial transferring procedure is embedded by re-weighting the discriminator loss to improve the feature alignments between the two domains with imbalanced scales. Furthermore, a self-paced training procedure is performed to re-train the detector by alternately generating pseudo-labels in the target domain from easy to hard. In our experiments, an empirical analysis of the proposed framework is conducted by evaluating performance in various datasets such as Cityscapes and VOC0712, and the results verify the higher accuracy and effectiveness of the proposed detector in comparison with state-of-the-art detectors.

Pre-processing approaches for imbalanced distributions in regression

Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.

An Evolutionary UnderBagging Approach to Tackle the Survival Prediction of Trauma Patients: A Case Study at the Hospital of Navarre

Survival prediction systems are used among emergency services at hospitals in order to measure their quality objectively. In order to do so, the estimated mortality rate given by a prediction model is compared with the real rate of the hospital. Hence, the accuracy of the prediction system is a key factor as more reliable estimations can be obtained. Survival prediction systems are aimed at scoring the severity of patients' injuries. Afterward, this score is used to estimate whether the patient will survive or not. Luckily, the number of patients who survive their injuries is greater than that of those who die. However, this degree of imbalance implies a greater difficulty in learning the prediction models. The aim of this paper is to develop a new prediction system for the Hospital of Navarre with the goal of improving the prediction capabilities of the currently used models since it would imply having a more reliable measurement of its quality. In order to do so, we propose a new strategy to conform an ensemble of classifiers using an evolutionary under sampling process in the bagging methodology. The experimental study is carried out over 462 patients who were treated at the Hospital of Navarre. Our new ensemble approach is an appropriate tool to deal with this problem as it is able to outperform the currently used models by the staff of the hospital as well as several state-of-the-art ensemble approaches designed for imbalanced domains.

Resampling with neighbourhood bias on imbalanced domains

AbstractImbalanced domains are an important problem that arises in predictive tasks causing a loss in the performance on the most relevant cases for the user. This problem has been extensively studied for classification problems, where the target variable is nominal. Recently, it was recognized that imbalanced domains occur in several other contexts and for multiple tasks, such as regression tasks, where the target variable is continuous. This paper focuses on imbalanced domains in both classification and regression tasks. Resampling strategies are among the most successful approaches to address imbalanced domains. In this work, we propose variants of existing resampling strategies that are able to take into account the information regarding the neighbourhood of the examples. Instead of performing sampling uniformly, our proposals bias the strategies to reinforce some regions of the data sets. With an extensive set of experiments, we provide evidence of the advantage of introducing a neighbourhood bias in the resampling strategies for both classification and regression tasks with imbalanced data sets.

Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation

Liver transplantation is a promising and widely-accepted treatment for patients with terminal liver disease. However, transplantation is restricted by the lack of suitable donors, resulting in significant waiting list deaths. This paper proposes a novel donor-recipient allocation system that uses machine learning to predict graft survival after transplantation using a dataset comprised of donor-recipient pairs from the King’s College Hospital (United Kingdom). The main novelty of the system is that it tackles the imbalanced nature of the dataset by considering semi-supervised learning, analysing its potential for obtaining more robust and equitable models in liver transplantation. We propose two different sources of unsupervised data for this specific problem (recent transplants and virtual donor-recipient pairs) and two methods for using these data during model construction (a semi-supervised algorithm and a label propagation scheme). The virtual pairs and the label propagation method are shown to alleviate the imbalanced distribution. The results of our experiments show that the use of synthetic and real unsupervised information helps to improve and stabilise the performance of the model and leads to fairer decisions with respect to the use of only supervised data. Moreover, the best model is combined with the Model for End-stage Liver Disease score (MELD), which is at the moment the most popular assignation methodology worldwide. By doing this, our decision-support system considers both the compatibility of the donor and the recipient (by our prediction system) and the recipient severity (via the MELD score), supporting then the principles of fairness and benefit.

Domain Knowledge and Team Creativity: A Typological View

This study presents a typological view of domain knowledge and creativity from teamwork perspective. With this in mind, the study reviews the literature on team members' collaboration, role centrality and its implications for team creativity. The study proposes a continuum of team creativity and different combinations of domain expertise possessed by the team members such that teams with more balanced domain expertise are more likely to produce more creative outcome than a team where its members consist imbalanced domain expertise. The study presents interesting avenues for future researchers

A Survey of Predictive Modeling on Imbalanced Domains

Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are highly relevant for end users (e.g., fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which, when associated with the rarity of some of them on the available training data, creates serious problems to predictive modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey, we discuss the main challenges raised by imbalanced domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.

Domain-Constraint Transfer Coding for Imbalanced Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) deals with the task that labeled training and unlabeled test data collected from source and target domains, respectively. In this paper, we particularly address the practical and challenging scenario of imbalanced cross-domain data. That is, we do not assume the label numbers across domains to be the same, and we also allow the data in each domain to be collected from multiple datasets/sub-domains. To solve the above task of imbalanced domain adaptation, we propose a novel algorithm of Domain-constraint Transfer Coding (DcTC). Our DcTC is able to exploit latent subdomains within and across data domains, and learns a common feature space for joint adaptation and classification purposes. Without assuming balanced cross-domain data as most existing UDA approaches do, we show that our method performs favorably against state-of-the-art methods on multiple cross-domain visual classification tasks.

Nearest Neighbor Classification with Locally Weighted Distance for Imbalanced Data

 Abstract—The datasets used in many real applications are highly imbalanced which makes classification problem hard. Classifying the minor class instances is difficult due to bias of the classifier output to the major classes. Nearest neighbor is one of the most popular and simplest classifiers with good performance on many datasets. However, correctly classifying the minor class is commonly sacrificed to achieve a better performance on others. This paper is aimed to improve the performance of nearest neighbor in imbalanced domains, without disrupting the real data distribution. Prototype-weighting is proposed, here, to locally adapting the distances to increase the chance of prototypes from minor class to be the nearest neighbor of a query instance. The objective function is, here, G-mean and optimization process is performed using gradient ascent method. Comparing the experimental results, our proposed method significantly outperformed similar works on 24 standard data sets.

Addressing imbalanced classification with instance generation techniques: IPADE-ID

A wide number of real word applications presents a class distribution where examples belonging to one class heavily outnumber the examples in the other class. This is an arduous situation where standard classification techniques usually decrease their performance, creating a handicap to correctly identify the minority class, which is precisely the case under consideration in these applications.In this work, we propose the usage of the Iterative Instance Adjustment for Imbalanced Domains (IPADE-ID) algorithm. It is an evolutionary framework, which uses an instance generation technique, designed to face the existing imbalance modifying the original training set. The method, iteratively learns the appropriate number of examples that represent the classes and their particular positioning. The learning process contains three key operations in its design: a customized initialization procedure, an evolutionary optimization of the positioning of the examples and a selection of the most representative examples for each class.An experimental analysis is carried out with a wide range of highly imbalanced datasets over the proposal and recognized solutions to the problem. The results obtained, which have been contrasted through non-parametric statistical tests, show that our proposal outperforms previously proposed methods.

Analysis of an evolutionary RBFN design algorithm, CO 2RBFN, for imbalanced data sets

In the classification problem field, we often encounter many real application areas in which the data do not have an equitable distribution among the different classes of the problem. In such cases, we are dealing with the so-called imbalanced data sets. This scenario has significant interest since standard classifiers are often biased towards the majority classes, whereas the minority ones tend to have a higher reward as they usually define the concepts of interest from the learning point of view. The aim of this paper is to analyse the performance of CO 2RBFN, a evolutionary cooperative–competitive model for the design of radial-basis function networks applied to classification problems on imbalanced domains, and to study its cooperation with a well-known pre-processing method, the “synthetic minority over-sampling technique”. The good performance of CO 2RBFN is shown through an experimental study carried out on a large collection of imbalanced data sets where we compare, by means of a proper statistical study, the behaviour of our model with many representative neural networks algorithms, the C4.5 decision tree and a hierarchical fuzzy rule-based classification system.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem.We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.