Abstract
ABSTRACT 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.
Published Version
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