A new approach to a maximum à posteriori-based kernel classification method.
This paper presents a new approach to a maximum a posteriori (MAP)-based classification, specifically, MAP-based kernel classification trained by linear programming (MAPLP). Unlike traditional MAP-based classifiers, MAPLP does not directly estimate a posterior probability for classification. Instead, it introduces a kernelized function to an objective function that behaves similarly to a MAP-based classifier. To evaluate the performance of MAPLP, a binary classification experiment was performed with 13 datasets. The results of this experiment are compared with those coming from conventional MAP-based kernel classifiers and also from other state-of-the-art classification methods. It shows that MAPLP performs promisingly against the other classification methods. It is argued that the proposed approach makes a significant contribution to MAP-based classification research; the approach widens the freedom to choose an objective function, it is not constrained to the strict sense Bayesian, and can be solved by linear programming. A substantial advantage of our proposed approach is that the objective function is undemanding, having only a single parameter. This simplicity, thus, allows for further research development in the future.
Department of International Development Engineering, Graduate School of Science and Engineering, Tokyo Institute of Technology, South 6th Building, Ookayama, Meguro-ku, Tokyo 152-8552, Japan. nopriadi@yahoo.com
Nopriadi
Yukihiko
Y
Yamashita
D016208:Databases, Factual / Q000145:classification* / Q000639:trends; D005544:Forecasting; D016014:Linear Models; D010363:Pattern Recognition, Automated / Q000145:classification* / Q000379:methods / Q000639:trends