Feature selection (FS) is an important data preprocessing technique in data mining and machine learning, which aims to select a small subset of information features to increase the performance and reduce the dimensionality. Particle swarm optimization (PSO) has been successfully applied to FS due to being efficient and easy to implement. However, most of the existing PSO-based FS methods face the problems of trapping into local optima and computationally expensive high-dimensional data. Multifactorial optimization (MFO), as an effective evolutionary multitasking paradigm, has been widely used for solving complex problems through implicit knowledge transfer between related tasks. Inspired by MFO, this study proposes a novel PSO-based FS method to solve high-dimensional classification via information sharing between two related tasks generated from a dataset. To be specific, two related tasks about the target concept are established by evaluating the importance of features. A new crossover operator, called assortative mating, is applied to share information between these two related tasks. In addition, two mechanisms, which are variable-range strategy and subset updating mechanism, are also developed to reduce the search space and maintain the diversity of the population, respectively. The results show that the proposed FS method can achieve higher classification accuracy with a smaller feature subset in a reasonable time than the state-of-the-art FS methods on the examined high-dimensional classification problems.

Feature selection (FS) is an important preprocessing technique for improving the quality of feature sets in many practical applications. Particle swarm optimization (PSO) has been widely used for FS due to being efficient and easy to implement. However, when dealing with high-dimensional data, most of the existing PSO-based FS approaches face the problems of falling into local optima and high-computational cost. Evolutionary multitasking is an effective paradigm to enhance global search capability and accelerate convergence by knowledge transfer among related tasks. Inspired by evolutionary multitasking, this article proposes a multitasking PSO approach for high-dimensional FS. The approach converts a high-dimensional FS task into several related low-dimensional FS tasks, then finds an optimal feature subset by knowledge transfer between these low-dimensional FS tasks. Specifically, a novel task generation strategy based on the importance of features is developed, which can generate highly related tasks from a dataset adaptively. In addition, a new knowledge transfer mechanism is presented, which can effectively implement positive knowledge transfer among related tasks. The results demonstrate that the proposed method can evolve a feature subset with higher classification accuracy in a shorter time than other state-of-the-art FS methods on high-dimensional classification.

Imbalanced data constitute a significant challenge in intelligent fault diagnosis cases because they can result in degraded diagnosis accuracy, which can in turn jeopardize the safety and reliability of industrial equipment. Generative adversarial networks (GANs) have been effectively used as common data augmentation methods to address this issue. However, their training process is difficult to perform and prone to mode collapse. Therefore, this article proposes a novel data augmentation method grounded in a diffusion model. The proposed method generates samples through physical simulation rather than adversarial training, which avoids the instability and mode collapse issues faced by GANs, leading to a more stable training process. Moreover, the proposed method utilizes the characteristics of gradual diffusion and random sampling to enhance the authenticity and diversity of sample generation. In addition, in terms of evaluating generation models, most existing works do not have a unified and thorough evaluation framework. Therefore, a comprehensive evaluation framework is proposed to effectively and comprehensively evaluate the performance of data augmentation models. Finally, the proposed method is evaluated using an open-source dataset and two actual testbeds to validate its effectiveness. The experimental results show that our method can generate higher quality and more diverse pseudosamples, and achieve superior fault diagnosis performance under imbalanced data. Specifically, our approach achieves diagnosis accuracies of 97.00%, 96.48%, and 98.30% on the three different datasets, all of which are superior to those of the compared state-of-the-art data augmentation algorithms.

Classification data are usually represented by many features, but not all of them are useful. Without domain knowledge, it is challenging to determine which features are useful. Feature selection is an effective preprocessing technique for enhancing the discriminating ability of data, but it is a difficult combinatorial optimization problem because of the challenges of the huge search space and complex interactions between features. Particle swarm optimization (PSO) has been successfully applied to feature selection due to its efficiency and easy implementation. However, most existing PSO-based feature selection methods still face the problem of falling into local optima. To solve this problem, this article proposes a novel PSO-based feature selection approach, which can continuously improve the quality of the population at each iteration. Specifically, a correlation-guided updating strategy based on the characteristic of data is developed, which can effectively use the information of the current population to generate more promising solutions. In addition, a particle selection strategy based on a surrogate technique is presented, which can efficiently select particles with better performance in both convergence and diversity to form a new population. Experimental comparing the proposed approach with a few state-of-the-art feature selection methods on 25 classification problems demonstrate that the proposed approach is able to select a smaller feature subset with higher classification accuracy in most cases.

The classification of heartbeats is an important method for cardiac arrhythmia analysis. This study proposes a novel heartbeat classification method using hybrid time-frequency analysis and transfer learning based on ResNet-101. The proposed method has the following major advantages over the afore-mentioned methods: it avoids the need for manual features extraction in the traditional machine learning method, and it utilizes 2-D time-frequency diagrams which provide not only frequency and energy information but also preserve the morphological characteristic within the ECG recordings, and it owns enough deep to make better use of performance of CNN. The method deploys a hybrid time-frequency analysis of the Hilbert transform (HT) and the Wigner-Ville distribution (WVD) to transform 1-D ECG recordings into 2-D time-frequency diagrams which were then fed into a transfer learning classifier based on ResNet-101 for two classification tasks (i.e., 5 heartbeat categories assigned by the ANSI/AAMI standard (i.e., N, V, S, Q and F) and 14 original beat kinds of the MIT/BIH arrhythmia database). For 5 heartbeat categories classification, the results show the F1-score of N, V, S, Q and F categories are F$_{N}$ 0.9899, F$_{V}$ 0.9845, F$_{S}$ 0.9376, F$_{Q}$ 0.9968, F$_{F}$ 0.8889, respectively, and the overall F1-score is 0.9595 using the combination data balancing. The results show the average values for accuracy, sensitivity, specificity, predictive value and F1-score on test set for 14 beat kinds the MIT-BIH arrhythmia database are 99.75%, 91.36%, 99.85%, 90.81% and 0.9016, respectively. Compared with other methods, the proposed method can yield more accurate results.

The fault diagnosis of rolling bearings is vital for the safe and reliable operation of mechanical equipment. However, the imbalanced data collected from the real engineering scenario bring great challenges to the deep learning-based diagnosis methods. For this purpose, this article proposes a methodology called full attention Wasserstein generative adversarial network (WGAN) with gradient normalization (FAWGAN-GN) for data augmentation and uses a shallow 1-D convolutional neural network (CNN) to perform fault diagnosis. First, a gradient normalization (GN) is introduced into the discriminator as a model-wise constraint to make it more flexible in setting the structure of the network, which leads to a more stable and faster training process. Second, the full attention (FA) mechanism is utilized to let the generator pay more attention to learning the discriminative features of the original data and generate high-quality samples. Third, to more thoroughly and deeply evaluate the data generation performance of generative adversarial networks (GANs), a more comprehensive multiple indicator-based evaluation framework is developed to avoid the one-sidedness and superficiality of using one or two simple indicators. Based on two widely applied fault diagnosis datasets and a real rolling bearing fault diagnosis testbed, extensive comparative fault diagnosis experiments are conducted to validate the effectiveness of the proposed method. Experimental results reveal that the proposed FAWGAN-GN can effectively solve the sample imbalance problem and outperforms the state-of-the-art imbalanced fault diagnosis methods.