Multi-label classification has attracted increasing attention for use in various application scenarios, such as medical diagnosis and semantic annotation. A large number of algorithms have been proposed for multi-label classification where many are ensemble-based. However, these ensemble-based methods usually employ bagging schemes for ensemble construction, with comparatively few stacked ensembles for multilabel classification. Existing research on stacked ensemble schemes remains active, but several issues remain such as (1) little has been done to learn the weights of classifiers for combined classifier selection; (2) pairwise label correlations is not investigated sufficiently to improve classification performance. To address these issues, we propose a novel approach that simultaneously exploits label correlations and the process of learning classifier weights to improve the existing stacked ensemble schemes. First, we introduce a weighted stacked ensemble for multi-label classification and use sparsity for regularization to facilitate classifier selection and ensemble construction. Second, we consider pairwise label correlations for assigning high similar weights to improve the classification performance. Finally, we develop an optimization algorithm based on the accelerated proximal gradient and the block coordinate descent techniques to find the optimal solution efficiently. Extensive experiments on publicly available datasets and real Cardiovascular and Cerebrovascular Disease datasets demonstrate that our proposed algorithm outperforms related state-of-the-art methods.

Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases, and the number of AD patients has increased year after year with the global aging trend. The onset of AD has a long preclinical stage. If doctors can make an initial diagnosis in the mild cognitive impairment (MCI) stage, it is possible to identify and screen those at a high-risk of developing full-blown AD, and thus the number of new AD patients can be reduced. However, there are problems with the medical datasets including AD data, such as insufficient number of samples and different data distributions. Transfer learning, which can effectively solve the problem of distribution discrepancy between training and test data and an insufficient number of target samples, has attracted increasing attention over recent years. In this paper, we propose a multi-source ensemble transfer learning (METL) approach by introducing ensemble learning and our tritransfer model that uses Tri-Training, which ensures the transferability of source data by the tri-transfer model and high performance through ensemble learning. The experimental results on the benchmark and AD datasets demonstrate that our proposed approach has effective transferability, robustness, and feasibility, and is superior to existing algorithms. Based on METL, we propose an auxiliary diagnosis system for the initial diagnosis of AD, which helps doctors identify patients in the MCI stage as quickly as possible and with high accuracy so that measures can be taken to prevent or delay the occurrence of AD.

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As a two-dimensional electromagnetic metamaterial, the cross-polarization conversion (CPC) metasurface is thin, easy to develop, and has attracted wide attention. However, existing CPC cell surface designs still rely on inefficient full-wave numerical simulation. Although some researchers have explored deep learning CPC metasurface structure design methods, the generated metasurface patterns are of poor quality. In this paper, an on-demand design method for cross-polarization conversion metasurface based on depth-generation model is proposed. Firstly, Wasserstein generative adversarial network (WGAN) is used to reverse design CPC metasurface, and Wasserstein distance is introduced to replace JS divergence and KL divergence to optimize the target. The problem of training difficulty caused by gradient elimination of original generative adversarial network (GAN) is fundamentally solved. Secondly, in the WGAN model, U-Net architecture generator is used to generate images, which greatly improves the surface image quality of CPC. In addition, a simulator composed of convolutional neural network (CNN) is also added in this paper to carry out forward prediction of S-parameter spectrum diagram. By inputting the patterns generated by WGAN into the simulator, the corresponding S-parameter spectrum diagram is generated and compared with the real S-parameter spectrum diagram, so as to verify whether the surface patterns of generated elements meet the requirements. The depth generation model proposed in this paper organically combines the forward spectrum prediction model and the reverse CPC metasurface structure design model, so that the CPC metasurface structure satisfying the expected electromagnetic response can be designed quickly on demand. This on-demand design method is expected to promote the rapid design, fabrication and application of electromagnetic devices.

Deep learning has achieved remarkable progress for visual recognition on balanced data sets but still performs poorly on real‐world long‐tailed data distribution. The existing methods mainly decouple the problem into the two‐stage decoupling training, that is, representation learning and classifier training, or multistage training based on knowledge distillation, thus resulting in huge training steps and extra computation cost. In this paper, we propose a conceptually simple yet effective One‐stage Long‐tailed Self‐Distillation framework, called OLSD, which simultaneously takes representation learning and classifier training into one‐stage training. For representation learning, we take two different sampling distributions and mixup them to input them into two branches, where the collaborative consistency loss is introduced to train network consistency, and we theoretically show that the proposed mixup naturally generates a tail‐majority distribution mixup. For classifier training, we introduce balanced self‐distillation guided knowledge transfer to improve generalization performance, where we theoretically show that proposed knowledge transfer implicitly minimizes not only cross‐entropy but also KL divergence between head‐to‐tail and tail‐to‐head. Extensive experiments on long‐tailed CIFAR10/100, ImageNet‐LT and multilabel long‐tailed VOC‐LT demonstrate the proposed method's effectiveness.

As a two-dimensional electromagnetic metamaterial, the cross-polarization conversion (CPC) metasurface is thin, easy to develop, and has attracted wide attention. However, existing CPC cell surface designs still rely on inefficient full-wave numerical simulation. Although some researchers have explored deep learning CPC metasurface structure design methods, the generated metasurface patterns are of poor quality. In this paper, an on-demand design method for cross-polarization conversion metasurface based on depth-generation model is proposed. Firstly, Wasserstein generative adversarial network (WGAN) is used to reverse design CPC metasurface, and Wasserstein distance is introduced to replace JS divergence and KL divergence to optimize the target. The problem of training difficulty caused by gradient elimination of original generative adversarial network (GAN) is fundamentally solved. Secondly, in the WGAN model, U-Net architecture generator is used to generate images, which greatly improves the surface image quality of CPC. In addition, a simulator composed of convolutional neural network (CNN) is also added in this paper to carry out forward prediction of S-parameter spectrum diagram. By inputting the patterns generated by WGAN into the simulator, the corresponding S-parameter spectrum diagram is generated and compared with the real S-parameter spectrum diagram, so as to verify whether the surface patterns of generated elements meet the requirements. The depth generation model proposed in this paper organically combines the forward spectrum prediction model and the reverse CPC metasurface structure design model, so that the CPC metasurface structure satisfying the expected electromagnetic response can be designed quickly on demand. This on-demand design method is expected to promote the rapid design, fabrication and application of electromagnetic devices.