An Improved Separating Hyperplane Method with Application to Embedded Intelligent Devices

Abstract

Classification is a common task in pattern recognition. Classifiers used in embedded intelligent devices need a good trade-off between prediction accuracy, resource consumption and prediction speed. Support vector machine(SVM) is accurate but its run-time complexity is higher due to the large number of support vectors. A new separating hyperplane method (NSHM) for the binary classification task was proposed. NSHM allows fast classification. However, NSHM is order-sensitive and this affects its classification accuracy. Inspired by NSHM, we propose CSHM, a combining separating hyperplane method. CSHM combines all optimal separating hyperplanes found by NSHM. Experimental results on UCI Machine Learning Repository show that, compared with NSHM and SVM, CSHM achieves a better trade-off between prediction accuracy, resource consumption and prediction speed.