Bayesian kernel adaptive grouping learning for multi-dimensional datasets

With the development of information technology, a large number of datasets with complex structures, such as multidimensional datasets, need to be processed and analyzed. In this paper we propose a kernel-based statistical learning algorithm, Bayesian Kernel Adaptive Grouping Learning (BKAGL), to provide an innovative solution for the classification problem of multi-dimensional datasets. BKAGL can integrate information from different dimensions adaptively. Meanwhile, we adopt the Bayesian framework which can infer the approximate posterior distributions of parameters. The utilization of grouping features can help find which groups of features have more contributions to the response. Simulation results illustrate that BKAGL outperforms some classical classification methods and the corresponding ungrouped method. The analysis of the electrocardiogram (ECG) and electroencephalography (EEG) datasets shows that BKAGL has the highest classification accuracy and provides explanatory information.