Imbalance Problem Research Articles

Respiratory disease detection in lung auscultation with convolutional neural networks and CVAE augmentation

The main purpose of this work was to investigate the possibility of detecting respiratory diseases in audio recordings of lung auscultation using modern deep learning tools, as well as to explore the possibility of using data augmentation by generating synthetic spectral representations of audio samples. The ICBHI (International Conference on Biomedical and Health Informatics) dataset was used for training, validation and augmentation. The dataset includes lung auscultations of 126 different subjects, there are a total of 920 sounds, of which 810 have signs of chronic diseases, 75 of non-chronic diseases and 35 with no pathology. The stage of data preprocessing includes discretization to 4kHz frequency, as well as filtering of frequency bands that do not carry information value for the task. In the next step, each sample was transformed into a frequency spectrum and Melspectrograms were generated. To solve the problem of class imbalance, the required number of synthetic spectrograms generated by convolutional variation autoencoders was added. At the stage of building the model, the methods of classical convolutional neural networks were used. The quality of the obtained algorithm was evaluated using a 10-fold cross-validation. Also, to assess the generalization of the proposed method, experiments were performed with the split of audio recordings into training and test sets using patient grouping. Qualitative evaluation of the model was performed using sensitivity, specificity, F1-score and Cohen’s kappa. A score of 98.45% F1-score was achieved for the 5-class classification problem which can contribute to the development of ways to synthesize and augment sensitive medical data. In addition, a cons of existing methods in the generalization of the obtained predictions were revealed, which opens the way for further research in the direction of clinical respiratory diseases detection.

Rotor fault diagnosis of centrifugal pumps in nuclear power plants based on CWGAN-GP-CNN for imbalanced dataset

As a crucial device in nuclear power plants, centrifugal pumps undertake the critical role of cooling water circulation. Centrifugal pump rotor misalignment and unbalanced faults cause pump performance degradation, vibration increase, and equipment damage, thus seriously affecting the safety and reliability of nuclear power plants. In the process of centrifugal pump rotor fault, the difficulty in obtaining data samples and the limited amount of data can lead to an imbalance problem between the quantity of normal state and fault state samples in the dataset. In order to solve the problem, this paper proposed a CWGAN-GP model for generating rotor fault data based on CGAN and WGAN-GP models, and combined it with a two-stream CNN model to realize the rotor fault diagnosis with an imbalanced dataset. The quality and performance of the data generated by the proposed method were evaluated and validated in terms of visualization analysis, statistical indicators, and comparison with different data generation models. The results show that the CWGAN-GP model can generate high-quality data. Meanwhile, compared with other models on datasets with different degrees of imbalance, the two-stream CNN model is more effective in fault diagnosis on the expanded dataset by the CWGAN-GP model, and the improvement of fault diagnosis accuracy ranges from 1.40% to 13.33%.

Adaptive group sample with central momentum contrast loss for unsupervised individual identification of cows in changeable conditions

Individual identification is the basis for accurate breeding and management of dairy cows. Most of the studies on individual identification of dairy cows focused on closed data sets. Meanwhile, the training process requires lots of manual labeling data as support. Aiming at solving the above shortcomings, an unsupervised cow individual identification model named AC-Cowid (Adaptive Group Sample-Central Momentum Contrastive loss-Cowid) was proposed. The innovation of the model lied in the novel sampler and loss function. Adaptive Group Sampler made up for the sampling imbalance problem in the current samplers. The sampler would self-update with model optimization. The central constraint term in Central Momentum Contrastive loss made the updated features more inclined to the cluster center, effectively solved the discomfort of Contrastive loss to cow image data. Feature similarity matching was adopted to meet the challenge of data conversion from a closed set to an open and changeable set. The unsupervised training reduced the workload of manual data labeling. The CMC-1 and mAP index of the final model were 99.50 % and 77.90 %, respectively. Ablation and hyper-parameter experiments proved the effectiveness of the proposed method. The comparison experiment tested eight unsupervised methods and five supervised algorithms in the state-of-the-art. The mAP and CMC-1 of the proposed method was the best among the comparison algorithms. Above all, the proposed method was 4.60 % and 5.38 % higher on mAP and CMC-1 compared with the best algorithm in supervised learning. This study could provide technical support for individual identification of cows in large-scale cattle farms.

Uncertainty quantification driven machine learning for improving model accuracy in imbalanced regression tasks

Several factors are known to determine the quality of machine learning models, one of which is the dataset quality. One problem related to the quality of a dataset is the imbalance issue. An imbalanced dataset contains significantly more data points for certain values of the output variable which increases the overfitting risk and negatively affects the prediction accuracy. In this article, we propose using epistemic uncertainty quantification (UQ) of machine learning models to identify rare samples in imbalanced regression problems for balancing the dataset. The developed algorithm, uncertainty quantification-driven imbalanced regression (UQDIR), is guided by UQ to restructure the training set with an adequate weight function using existent samples, eliminating the need for new data collection. After identifying rare samples with UQ, the algorithm selects a sample from the training set, assigns a resampling weight using the new weight function, and finally resamples the selected sample according to its assigned weight. We test UQDIR on several benchmark datasets and different machine learning algorithms, then compare its performance with similar imbalanced regression methods. A metamaterial design problem application is also provided for demonstrating the effectiveness of the algorithm in real-world scenarios. We show that improving the quality of UQ metrics results in improved model accuracy.

Contraband classification method for X-ray security images considering sample imbalance

X-ray security image contraband classification is widely used to assist in maintaining aviation and transportation security. This paper suggests an end-to-end X-ray security inspection image classification method that takes sample imbalance into account in order to address the issues of different scales of contraband in X-ray images, challenging samples, and unbalanced positive and negative samples inherent in passenger baggage security inspection. The feature fusion module is used to enhance the model's ability to express picture edge and texture features while the multi-scale feature extraction network is used to capture the features of numerous sorts of illegal goods with various scales. Based on the cost-sensitive idea, the loss function is designed to solve the problem of dataset imbalance, and improve the classification accuracy of difficult samples. experimental results of the subset constructed on the public dataset SIXray show that the proposed method improves the mean AP index by compared with the current optimal end-to-end classification model, especially for hard-to-classify samples such as scissors, the AP index has a significant improvement effect.

Reservation and allocation model considering the user cost and the utilization of parking space

In order to alleviate the problem of imbalance between parking supply and demand by improving the utilization rate of parking resources, and considering the game relationship between user benefits and system benefits in parking allocation, the optimization objective function of maximum parking space utilization and minimum user cost in parking reservation mode is established from the two aspects which are system optimization and user optimization respectively. Then, the optimal allocation parking integer programming model (OPA) considering users' preferences is established. An Augmented Lagrangian-Alternating Direction Method of Multipliers Algorithm is designed to solve the optimal solution of the model. Finally, in order to test the validity of the model, the proposed model and the classical distribution model are compared and analyzed under different supply and demand conditions. The results show that the performance of the proposed model in three performance metrics which are parking utilization, average user cost and request acceptance rate is significantly better than that of the classical allocation model. The research results of this paper can provide theoretical reference for the management of parking reservation platform.

A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss

Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images using deep learning techniques is a complex and challenging task One challenge is the complexity of fundus disease structures, which leads to low detection accuracy. Another challenge is the class imbalance problem common in multi-label image classification, which increases the difficulty of algorithm training and evaluation. To address these issues, this study leverages deep learning to propose an ophthalmic disease classification system. We first employ ResNet50 as the backbone network to extract image features, and then use our designed multi-dimensional attention module and adaptive scale discriminator to enhance the network's ability to detect disease features. During training, we innovatively propose a hybrid loss function method to improve the detection capability on imbalanced data. Finally, we conducted experiments on the ODRI-5K dataset with the proposed classification system. In the test set, our method achieved an AUC of 98.53 and an F1-score of 89.73. This result fully demonstrates the excellent disease classification capability of our method. In summary, the multi-label fundus image disease classification system we proposed exhibits outstanding recognition capability, providing an effective solution for the diagnosis of multi-label fundus image diseases.

Impact of Runner Size, Gate Size, Polymer Viscosity, and Molding Process on Filling Imbalance in Geometrically Balanced Multi-Cavity Injection Molding.

The injection molding process is one of the most widely used methods for polymer processing in mass production. Three critical factors in this process include the type of polymer, injection molding machines, and processing molds. Polypropylene (PP) is a widely used semi-crystalline polymer due to its favorable flow characteristics, including a high melt flow index and the absence of a need for a mold temperature controller. Additionally, PP exhibits good elongation and toughness, making it suitable for applications such as box hinges. However, its tensile strength is a limitation; thus, glass fiber is added to enhance this property. It is important to note that the incorporation of glass fiber increases the viscosity of PP. Multi-cavity molds are commonly employed to achieve cost-effective and efficient mass production. The filling challenges associated with geometrically balanced layouts are well documented in the literature. These issues arise due to the varying shear rates of the melt in the runner. High shear rate melts lead to high melt temperatures, which decrease melt viscosity and facilitate easier flow. Consequently, this results in an imbalanced filling phenomenon. This study examines the impact of runner size, gate size, polymer viscosity, and molding process on the filling imbalanced problem in multi-cavity injection molds. Tensile bar injection molding was performed using conventional injection molding (CIM) and microcellular injection molding (MIM) techniques. The tensile properties of the imbalanced multi-cavity molds were analyzed. Flow length within the cavity served as an indicator of the filling imbalance. Additionally, computer simulations were conducted to assess the shear rate's effect on the runner's melt temperature. The results indicated that small runner and gate sizes exacerbate the filling imbalance. Conversely, glass fiber-filled polymer composites also contribute to increased filling imbalance. However, foamed polymers can mitigate the filling imbalance phenomenon.

DSTM: A transformer-based model with dynamic-static feature fusion in speech emotion recognition

With the support of multi-head attention, the Transformer shows remarkable results in speech emotion recognition. However, existing models still suffer from the inability to accurately locate important regions in semantic information at different time scales. To address this problem, we propose a Transformer-based network model for dynamic-static feature fusion, composed of a locally adaptive multi-head attention module and a global static attention module. The locally dynamic multi-head attention module adapts the attention window sizes and window centers of the different regions through speech samples and learnable parameters, enabling the model to adaptively discover and pay attention to valuable information embedded in speech. The global static attention module enables the model to use each element in the sequence fully and learn critical global feature information by establishing connections over the entire input sequence. We also use the data mixture training method to train our model and introduce the CENTER LOSS function to supervise the training of the model, which can better speed up the fitting speed of the model and alleviate the sample imbalance problem to a certain extent. This method achieved good performance on the IEMOCAP and MELD datasets, proving that our proposed model structure and method have better accuracy and robustness.

Principals Leadership in Improving Students Entrepreneurial Competence

The problem of imbalance between the number of labor force growth and available jobs is a seriousconcern in the context of education and economic development today. The phenomenon where the number of Vocational High School (SMK) graduates continues to grow but their ability to become entrepreneurs or create new jobs is still minimal highlights the importance of effective entrepreneurship education. Currently, entrepreneurship education in schools tends to only touch the surface, focusing on introducing entrepreneurial values, without giving emphasis on internalizing these values or encouraging real actions in daily life. Based on these problems, this paper will discuss how the principal's leadership and how the principal's leadership strategy in improving student entrepreneurship in SMK. The research process using literature study begins with the identification of the topic or research question to be researched. After that, researchers search for related literature through various sources of information such as libraries, online databases, and electronic journals. With great responsibility, school principals are responsible for the implementation of learning activities that can ultimately improve existing graduates, especially in the field of entrepreneurship. In line with this, some of the strategies that can be carried out by school principals in improving student entrepreneurial competence are by creating innovation, working hard in improving school quality, motivation, never giving up, and having entrepreneurial instincts.

Imbalanced image classification algorithm based on fine-grained analysis

Fine-grained attribute analysis and data imbalance have always been research hotspots in the field of computer vision. Due to the complexity and diversity of fine-grained attribute images, traditional image classification methods have shortcomings in paying attention to fine-grained attributes of images and perform poorly when dealing with imbalanced data sets. To overcome these problems, this study proposes a fine-grained image threshold classification algorithm based on deep metric learning. By introducing a metric learning method, the focus on fine-grained attributes of images is enhanced. At the same time, by applying pairwise loss and proxy loss, the classification accuracy of the model is improved and the model convergence speed is accelerated. In order to deal with the problem of data imbalance, a classifier based on threshold analysis is designed. The classifier uses threshold analysis technology to achieve multi-level classification of fine-grained images, thereby improving the problem of low classification accuracy of a few categories in imbalanced data sets. Experimental results show that the proposed fine-grained image threshold classification algorithm based on deep metric learning is significantly superior to other methods in terms of classification accuracy.

A novel reinforcement learning agent for rotating machinery fault diagnosis with data augmentation

The problem of data imbalance and limited sample poses a prominent challenge to fault diagnosis in industry, especially in rotating machinery fault diagnosis due to the constraints of sampling and labeling. Reinforcement learning is known for its auto-optimized learning ability but it is severely limited by the quality and quantity of data it is able to collect. This paper applies the Equilibrium Deep Q-Network (Equilibrium-DQN) based agent with the Variational Autoencoder with Wasserstein Generative Adversarial Network and Gradient Penalty (VAE-WGAN-GP) for rotating machinery fault diagnosis. The Equilibrium-DQN possesses the property of leveraging multiple target networks to improve training stability and accuracy. The VAE-WGAN-GP excels in resolving data scarcity problems in fault diagnosis, especially in enriching labeled data with more typical samples while keeping the original data features. Herein, a novel framework is presented that integrates both data augmentation techniques with reinforcement learning agent for fault diagnosis. The proposed framework improves the weakness of insufficient samples in fault diagnosis and reveals its potential in complex industrial applications. The experiments, both on the test bench dataset and on the real locomotive dataset, confirm the advantage of the proposed framework.

Fault diagnosis of planetary gearboxes under small and imbalanced samples based on enhanced Siamese network with improved downsampling module

Due to the limitations of the operating conditions and equipment status, in practice, the normal data for planetary gearboxes are often sufficient, but the fault data are scarce and difficult to obtain, causing the fault diagnosis to face not only an imbalance problem but also a small sample problem. It leads to serious misdiagnosis and poor generalizability. To overcome above challenges, an enhanced Siamese network with improved downsampling module is proposed to reduce the negative impact of small and imbalanced datasets on fault diagnosis. Firstly, the Siamese network is combined with data enhancement to adaptively increase the fault training data with different small sample degrees. It can make the number of training data of each class get rid of small sample, while avoiding the excessive use of scarce fault data causing additional overfitting, so as to improve the generalization of the model. Among them, the data enhancement adopts the extreme noise expansion method proposed in this work, which can quickly generate samples that meet the requirements and help to improve the performance of the model in noisy environments. Secondly, adding the improved downsampling module to the Siamese network. The improved downsampling module introduces density-based spatial clustering of applications with noise (DBSCAN) and distribution ratio calculation, which can consider the distribution range and density of data when selecting. It can not only balance the training data, but also avoid serious information loss, and then reduce the probability of misdiagnosis. Finally, using six imbalanced planetary gearbox datasets with different small sample degrees for verification. The experimental results show that the proposed method can efficiently increase and balance the training data, which greatly improves the accuracy of fault diagnosis under the condition of small and imbalanced samples, especially when the small sample degree is very serious, the accuracy is improved by 57.76% on average.

A category incremental continuous learning model for imbalance arrhythmia detection

Abstract The development of efficient arrhythmia detection systems is crucial for physiological measurements and computer-aided diagnosis. Existing systems rely mainly on offline learning methods and lack the ability to assimilate new data or recognize emerging categories. To address these challenges, this study introduces an arrhythmia detection model that is resistant to data imbalance and has continuous learning capabilities, specifically for incremental learning of new ECG data categories. The system incorporates constraints derived from the new class data and implements a dynamic mechanism for updating connection weights, facilitating the incremental continual learning of classes. Confronted with the problem of models forgetting the original data and overfitting with the added data in continuous learning, we introduce a data balancing method by regularization to balance the model's memory and learning of the two types of data. Facing the data imbalance problem in continuous learning, we introduce a posteriori probability weighting strategy. This strategy assigns greater importance to high-value samples based on the model's posterior residual kernel density estimates. Comprehensive testing of the model using various datasets from the MIT-BIH database indicates superior performance in incremental learning tasks. The results reveal that the model not only excels in class incremental learning but also ensures effective balancing across different data classes.

From shallows to depths: unveiling hybrid synthetic data modeling for enhanced learning with privacy considerations in naturally imbalanced datasets

Data serves as the foundational element that drives model development and performance in machine learning and deep learning. The learning algorithms are as good as data on which they are trained on. Data constrained environments pose serious threat to the effectiveness of learning algorithms. Data limitations stem from a range of factors, including regulatory restrictions, privacy concerns, and the inherent scarcity of relevant data. This constrained availability of data often leads to class imbalance problem in the context of classification tasks. To address the challenges of limited data, the algorithmic generated data, also known as synthetic data, is gaining significant traction as a cost-effective, readily available, and secure alternative. Synthetic data generation techniques can be employed to enhance dataset size by augmenting data samples and to address class imbalance by increasing the number of minority class instances. These techniques generally fall into two main categories: distance-based shallow models and probability estimation-based deep generative models. Shallow interpolation-based models generate new data points within the local space between existing data points, while deep density estimation based models generate new data by learning the whole distribution of data. . In the context of smaller datasets, deep generative models often struggle to accurately estimate the probability distribution of whole data. To effectively represent the global data distribution, these deep models require initial starting data samples to guide their approximation. This paper examines the potential of integration of shallow and deep generative models in the data generation pipeline for effective synthetic data augmentation which furthers enhanced learning and generalization of downstream tasks. In this work, we present a hybrid approach of tabular data generation involving mixed type data attributes (continuous, discrete) and pay special attention to data imbalance and insufficient data problems. We introduce the Hybrid Data Balancing and Augmentation Approach for Mixed Tabular Data (HDBA-MTD), specifically designed to synthesize samples for underrepresented labels of output class and address issues of insufficient data instances. This approach enhances training data diversity, thereby paying special attention to the downstream classification and generalization performance. Experiments are carried out using benchmark datasets to assess the practicality of the presented hybrid model in real-world scenarios. This work has also attempted to quantify the privacy preservability for real data concerning ethical considerations and data security circumstances. The evaluation and analysis of these experiments show that the present hybrid model performs favorably compared to other current hybrid synthetic data generation methods.

Predicting Multiple Outcomes Associated with Frailty based on Imbalanced Multi-label Classification.

Frailty syndrome is prevalent among the elderly, often linked to chronic diseases and resulting in various adverse health outcomes. Existing research has predominantly focused on predicting individual frailty-related outcomes. However, this paper takes a novel approach by framing frailty as a multi-label learning problem, aiming to predict multiple adverse outcomes simultaneously. In the context of multi-label classification, dealing with imbalanced label distribution poses inherent challenges to multi-label prediction. To address this issue, our study proposes a hybrid resampling approach tailored for handling imbalance problems in the multi-label scenario. The proposed resampling technique and prediction tasks were applied to a high-dimensional real-life medical dataset comprising individuals aged 65years and above. Several multi-label algorithms were employed in the experiment, and their performance was evaluated using multi-label metrics. The results obtained through our proposed approach revealed that the best-performing prediction model achieved an average precision score of 83%. These findings underscore the effectiveness of our method in predicting multiple frailty outcomes from a complex and imbalanced multi-label dataset.

VGTS: Visually Guided Text Spotting for novel categories in historical manuscripts

In the field of historical manuscript research, scholars frequently encounter novel symbols in ancient texts, investing considerable effort in their identification and documentation. Although existing object detection methods achieve impressive performance on known categories, they struggle to recognize novel symbols without retraining. To address this limitation, we propose a Visually Guided Text Spotting (VGTS) approach that accurately spots novel characters using just one annotated support sample. The core of VGTS is a spatial alignment module consisting of a Dual Spatial Attention (DSA) block and a Geometric Matching (GM) block. The DSA block aims to identify, focus on, and learn discriminative spatial regions in the support and query images, mimicking the human visual spotting process. It first refines the support image by analyzing inter-channel relationships to identify critical areas, and then refines the query image by focusing on informative key points. The GM block, on the other hand, establishes the spatial correspondence between the two images, enabling accurate localization of the target character in the query image. To tackle the example imbalance problem in low-resource spotting tasks, we develop a novel torus loss function that enhances the discriminative power of the embedding space for distance metric learning. To further validate our approach, we introduce a new dataset featuring ancient Dongba hieroglyphics (DBH) associated with the Naxi minority of China. Extensive experiments on the DBH dataset and other public datasets, including Egyptian Hieroglyph (EGY), Historical Arabic Documents (HAD), Tripitaka Koreana in Han (TKH), and Notary Charters (NC), show that VGTS consistently surpasses state-of-the-art methods. The proposed framework exhibits great potential for application in historical manuscript text spotting, enabling scholars to efficiently identify and document novel symbols with minimal annotation effort.

Taking relations as known conditions: A tagging based method for relational triple extraction

Relational triple extraction refers to extracting entities and relations from natural texts, which is a crucial task in the construction of knowledge graph. Recently, tagging based methods have received increasing attention because of their simple and effective structural form. Among them, the two-step extraction method is easy to cause the problem of category imbalance. To address this issue, we propose a novel two-step extraction method, which first extracts subjects, generates a fixed-size embedding for each relation, and then regards these relations as known conditions to extract the objects directly with the identified subjects. In order to eliminate the influence of irrelevant relations when predicting objects, we use a relation-special attention mechanism and a gate unit to select appropriate relations. In addition, most current models do not account for two-way interaction between tasks, so we design a feature interactive network to achieve bidirectional interaction between subject and object extraction tasks and enhance their connection. Experimental results on NYT, WebNLG, NYT⋆ and WebNLG⋆ datasets show that our model is competitive among joint extraction models.

Credit risk assessment method driven by asymmetric loss function

Credit risk assessment is significantly hindered by the problem of class imbalance, and cost-sensitive methods represent an effective strategy to address this issue. However, most algorithms tend to approach the imbalance from a class perspective, overlooking the finer details at the sample level. Moreover, such methods are susceptible to interference from noise and outliers. In response to these challenges, this paper proposes an asymmetric cost-sensitive support vector machine (QTSVM) that combines the quadratic type squared error loss function (QTSELF) with support vector machine (SVM). It not only leverages the asymmetry of the loss function by applying varying penalties based on classification errors but also employs different processing measures for samples from different classes. Additionally, it enhances model robustness by imposing a tiny penalty on noise or outliers. The adaptive moment estimation (Adam) algorithm is employed to optimize QTSVM. Extensive experiments and statistical tests profoundly demonstrate the superior performance of QTSVM.

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

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