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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