Multi-weighted majority voting algorithm on support vector machine and its application
Cheng-Ho Huang, Jhing-Fa Wang · 2009
The important issue in multi-class classification on support vector machines is the decision rule, which determines whether an input pattern belongs to a predicted class. To enhance the accuracy of multi-class classification, this study proposes a multi-weighted majority voting algorithm of support vector machine (SVM), and applies it to overcome complex facial security application. The proposed algorithm consists of two parts: the hierarchical classification method and the multi-weighted majority voting strategy. The proposed hierarchical classification method is an SVM assembled method to create relationally hierarchical subsets to every class; the proposed multi-weighted majority voting strategy constructs multiple decision terms to estimate the performance of the decision fusion. According to experiments on the application, the performance of FRR and FAR as 1.14% and 1.28%, respectively.