Ensemble methods are widely used to tackle class imbalance problem. However, for existing imbalanced ensemble (IE) methods, the samples in each subset are resampled from the same dataset, and are directly input to the classifier for training, so the quality (diversity and separability) of the subsets is unsatisfactory usually. To solve the problem, a deep fuzzy envelope sample generation mechanism is proposed. First, the fuzzy C-means clustering based deep sample envelope prenetwork (DSEN) is designed to mine correlation information among samples, thereby increasing the quality of the subsets. Second, the local manifold structure metric and global structure distribution metric are designed to construct local-global structure consistency mechanism (LGSCM) to enhance distribution consistency of interlayer samples of DSEN. Third, the DSEN and LGSCM are combined to form the final deep sample envelope network–DSENLG to refresh the existing subsets. Finally, base classifiers are applied on the new subsets generated by the DSENLG and then fused, thereby realizing a new IE algorithm. The experimental results show that the proposed algorithm is significantly better than existing representative IE algorithms and it achieves the highest improvement of 10.64%, 19.5%, 18.67% and 22.33% on four criteria over the state-of-the-art methods. The originality of the article is threefold: proposing the concept of “deep fuzzy samples” or “envelope samples”, which comprehensively considers the correlation information among original samples; proposing the LGSCM to resolve the distribution inconsistency of interlayer samples; and forming an fuzzy envelope sample based IE algorithm.

Age estimation can obtain biological age which is helpful for diagnosis of healthy status and disease. The current age estimation methods do not consider the deep relationships of instances, which limits the potential improvement of the age estimation performance. A hierarchical age estimation mechanism with adaboost-based deep instance weighted fusion is proposed to solve this problem. First, a circulation iterative means clustering (CIMC) algorithm is designed for constructing the hierarchical instance space (multiple-layer instance spaces) and obtain multiple trained base regression models. Second, an adaboost-based deep instance weighted fusion (ADIWF) mechanism is designed to fuse the results of the trained regression models. Several representative age-related datasets are used for verification of the proposed method. The experimental results show that the mean absolute error (MAE) can be decreased apparently, by 6.86% and 1.42% on the Heart and Diabetes Dataset, respectively. Besides, some factors that may influence the performance of the proposed mechanism are studied. In general, the proposed age estimation mechanism is effective. In addition, the mechanism is a kind of framework mechanism, so it can be used to construct different concrete age estimation algorithms, and is helpful for related studies.

Speech feature learning is very important for the design of classification algorithm of Parkinson's disease (PD). Existing speech feature learning method for classification of PD just pays attention to the speech feature. This paper proposed a novel hybrid feature learning algorithm which puts the features of all the speech segments of each subject together, thereby obtaining new and high efficient features without feature transformation. Firstly, hybrid features was constructed by combining features and segments. Secondly, high efficient hybrid feature selection was conducted by various criteria. Thirdly, the selected hybrid features were applied for classification of PD. Besides, various evaluation criteria are introduced into feature selection in this manuscript. Experimental results show that this proposed algorithm can obtain new features (hybrid feature) with satisfactory classification accuracy. The selected features are very stable and meaningful.