Multiple Empirical Kernel Mapping Based Broad Learning System for Classification of Parkinson’s Disease With Transcranial Sonography

Abstract

Transcranial sonography (TCS) has become more popular for diagnosis of Parkinson's disease (PD), and the TCS-based computer-aided diagnosis (CAD) for PD also attracts considerable attention, in which classifier is a critical component. Broad learning system (BLS) is a newly proposed single layer feedforward neural network for classification. In BLS, the original input features are mapped to several new feature representations to form the feature nodes, and then these mapped features are expanded to enhancement nodes by random mapping in a wide sense. However, random mapping performed for enhancement nodes is too simple and the generated features lack interpretability together with relative low representation. In this work, we propose a multiple empirical kernel mapping (MEKM) based BLS (MEKM-BLS) algorithm, which adopts MEKM to map the data of feature nodes to enhancement nodes. MEKM-BLS then has more meaningful enhancement layer in feedforward neural network. Moreover, the experiment for PD diagnosis with TCS shows that MEKM-BLS achieves superior performance to the original BLS algorithm.