Work

Kernel-based multiple criteria linear programming classifier

Year: 2010

Type: conference-paper

Abstract: This paper proposed a novel classification model which introduced the kernel function into the original Multiple Criteria Linear Programming (MCLP) model. MCLP model is used as a classification method which can only solve linear separable problems in data mining. However, the proposed kernel-based MCLP model can deal with non-linear cases. Meanwhile, unlike some other complicated models, this model is effective and easy to understand. A couple of experimental tests were conducted to evaluate the performance of the proposed kernel-based MCLP model compared with the existing methods: original MCLP and SVM. The results show that kernel-based MCLP model is a competitive method in dealing with nonlinear separable classification problems.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2010.04.271

Authors: Zhan Zhang, Dongling Zhang, Yingjie Tian

Institutions: CITIC Group (China), Beijing Municipal Science and Technology Commission, Chinese Academy of Sciences

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Awards: G246224327, G2517777447, G2901761077, G4108382384