In an actual industrial scenario, machines typically operate normally for the majority of the time, with malfunctions occurring only occasionally. As a result, there is very little recorded data on defects. Consequently, the fault diagnostic dataset becomes imbalanced, with a significantly lower number of fault samples compared to normal samples. Furthermore, with the rapid development of the manufacturing industry, the increasing complexity of machines and equipment leads to various challenges in collecting fault data, such as noise, within-class imbalance, multi-class imbalance, and time series imbalance. It is worth noting that this study is the first to comprehensively summarize these four specific challenges. Therefore, addressing these issues has become a critical research focus and a pain point in the field of fault diagnosis, and numerous solutions have emerged. This study provides a comprehensive overview of these solutions at three levels: data preprocessing, feature extraction, and classifier improvement. It also describes the applications of imbalanced data classification methods, including pure resampling techniques, as well as sampling techniques that combine resampling algorithms with feature extraction and classifier improvement in industrial scenarios. Finally, we summarize the challenges facing imbalanced data classification research and suggest potential directions for future studies. 

Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring

This paper proposes a novel data augmentation scheme called the conditional generative adversarial network minority-class-augmented oversampling scheme (CTGAN-MOS) for solving class imbalance problems. Our methodology encompassed six key steps: data engineering using sophisticated pre-processing techniques, identifying the type of vulnerabilities present in the data, curating good quality synthetic data using the CTGAN model, the intelligent fusion of real and synthetic data, noise removal from the augmented data using coin-throwing algorithm, and building classifiers with the high-quality augmented data. Our scheme maintains higher structural similarity (data truthfulness) between the original and the resampled data by intelligently adding high-quality samples only to the minority class, whereas some augmentation techniques add records to the majority class, leading to poor-quality resampled data. Our scheme removes noisy samples from the data, which has remained unexplored in the CTGAN-based data augmentation. Furthermore, it augments data by adding fewer records compared to existing schemes, while offering comparable performance. Experiments are conducted on benchmark datasets to prove the feasibility of the proposed CTGAN-MOS in realistic scenarios. Results prove the improvement by CTGAN-MOS over existing state-of-the-art (SOTA) techniques in terms of accuracy, recall, precision, <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score, and <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>-mean score. Specifically, the CTGAN-MOS has yielded accuracy values of 100% and 99.83% on two datasets which are higher than recent SOTA techniques. On average, it has yielded the 22.58% and 29.47% improvements w.r.t. <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>-mean score on two different datasets. On average, it adds 8.26% and 26.01% fewer records than the existing SOTA methods in the two datasets. Lastly, our scheme yields highly balanced confusion matrices compared to recent SOTA data augmentation techniques.

CTGAN-MOS: Conditional Generative Adversarial Network Based Minority-Class-Augmented Oversampling Scheme for Imbalanced Problems

Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms. Over the past decades, these paradigms have revealed effective and flexible solutions to practical and complex problems. This paper summarizes the natural mechanisms of diverse advanced nature-inspired intelligent computing paradigms, which provide valuable lessons for building general-purpose machines capable of adapting to the environment autonomously. According to the natural mechanisms, we classify nature-inspired intelligent computing paradigms into 4 types: evolutionary-based, biological-based, social-cultural-based, and science-based. Moreover, this paper also illustrates the interrelationship between these paradigms and natural mechanisms, as well as their real-world applications, offering a comprehensive algorithmic foundation for mitigating unreasonable metaphors. Finally, based on the detailed analysis of natural mechanisms, the challenges of current nature-inspired paradigms and promising future research directions are presented.

Nature-inspired Intelligent Computing: A Comprehensive Survey

A clustering-based adaptive undersampling ensemble method for highly unbalanced data classification

Predicting early recurrence (ER) of hepatocellular carcinoma (HCC) accurately can guide treatment decisions and further enhance survival. Computed tomography (CT) imaging, analyzed by deep learning (DL) models combining domain knowledge, has been employed for the prediction. However, these DL models utilized late fusion, restricting the interaction between domain knowledge and images during feature extraction, thereby limiting the prediction performance and compromising decision-making interpretability. We propose a novel Vision Transformer (ViT)-based DL network, referred to as Dual-Style ViT (DSViT), to augment the interaction between domain knowledge and images and the effective fusion among multi-phase CT images for improving both predictive performance and interpretability. We apply the DSViT to develop pre-/post-operative models for predicting ER. Within DSViT, to balance the utilization between domain knowledge and images within DSViT, we propose an adaptive self-attention mechanism. Moreover, we present an attention-guided supervised learning module for balancing the contributions of multi-phase CT images to prediction and a domain knowledge self-supervision module for enhancing the fusion between domain knowledge and images, thereby further improving predictive performance. Finally, we provide the interpretability of the DSViT decision-making. Experiments on our multi-phase data demonstrate that DSViTs surpass the existing models across multiple performance metrics and provide the decision-making interpretability. Additional validation on a publicly available dataset underscores the generalizability of DSViT. The proposed DSViT can significantly improve the performance and interpretability of ER prediction, thereby fortifying the trustworthiness of artificial intelligence tool for HCC ER prediction in clinical settings. 

A knowledge-enhanced interpretable network for early recurrence prediction of hepatocellular carcinoma via multi-phase CT imaging

Unbalanced classification is an essential machine learning task, which has attracted widespread attention from both the academic and industrial communities due mainly to its broad applications. Evolutionary computation (EC) has contributed greatly to unbalanced classification. However, to the best of our knowledge, there have not been any comprehensive investigations on the strengths and weaknesses of alternative EC methods in addressing various challenging problems in unbalanced classification. This paper reviews the literature which utilize EC techniques for unbalanced classification, with the aim of revealing the contributions of EC to unbalanced classification, providing an overview of recent advances, and identifying limitations of existing works. In addition, we present a series of real-world applications, and identify open challenges as well as possible research directions for the future. 

A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?

An X-ray image classification method with fine-grained features for explainable diagnosis of pneumoconiosis

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A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?

An X-ray image classification method with fine-grained features for explainable diagnosis of pneumoconiosis