Ensemble deep random vector functional link network using privileged information for Alzheimer's disease diagnosis.

Abstract

In this paper, deep RVFL and its ensembles are enabled to incorporate privileged information, however, the standard RVFL model and its deep models are unable to use privileged information. Privileged information-based approach commonly seen in human learning. To fill this gap, we incorporate learning using privileged information (LUPI) in deep RVFL model and propose deep RVFL with LUPI framework (dRVFL+). Privileged information is available while training the models. To make the model more robust, we propose ensemble deep RVFL+ with LUPI framework (edRVFL+). Unlike traditional ensemble approach wherein multiple base learners are trained, the proposed edRVFL+ optimises a single network and generates an ensemble via optimization at different levels of random projections of the data. Both dRVFL+ and edRVFL+ efficiently utilise the privileged information which results in better generalization performance. In LUPI framework, half of the available features are used as normal features and rest as the privileged features. However, we propose a novel approach for generating the privileged information. To the best of our knowledge, this is first time that a separate privileged information is generated. The proposed models are employed for the diagnosis of Alzheimer's disease. Experimental results show the promising performance of both the proposed models.