A classifier for multi-dimensional datasets based on Bayesian multiple kernel grouping learning. Issue 11 (24th July 2019)

• Record Type: Journal Article
• Authors: Dong, Fangli; Wang, Xiaozhou
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 11(2019)
• Page Start: 2151
• Page End: 2174
• Publication Date: 2019-07-24
• Subjects: Multi-dimensional dataset -- classifier -- conjugate Bayesian framework -- multiple kernels -- interpretable; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1612395
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: This paper proposes an algorithm for the classification of multi-dimensional datasets based on the conjugate Bayesian Multiple Kernel Grouping Learning (BMKGL). Using conjugate Bayesian framework improves the computation efficiency. Multiple kernels instead of a single kernel avoid the kernel selection problem which is also a computationally expensive work. Through grouping parameter learning, BMKGL can simultaneously integrate information from different dimensions and find the dimensions which contribute more to the variations of the outcome for the purpose of interpretable property. Meanwhile, BMKGL can select the most suitable combination of kernels for different dimensions so as to extract the most appropriate measure for each dimension and improve the accuracy of classification results. The simulation results illustrate that our learning process has better performance in prediction results and stability compared to some popular classifiers, such as k -nearest neighbours algorithm, support vector machine algorithm and naive Bayes classifier. BMKGL also outperforms previous methods in terms of accuracy and interpretation for the heart disease and EEG datasets.