Author(s)

Nian Yan, Zhengxin Chen, Yong Shi, Zhenyuan Wang, Guimin Huang

College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE USA.
Department of Mathematics University of Nebraska at Omaha, Omaha, NE USA 68182.
Guilin University of Electronic Technology, Guilin, China.

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2ⁿ – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are relatively small number of training cases available (m<<2ⁿ－1). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered.

Nonlinear Programming; Nonlinear Classification; Non-Additive Measure; Choquet Integral; Support Vector Machines

N. Yan, Z. Chen, Y. Shi, Z. Wang and G. Huang, "Using Non-Additive Measure for Optimization-Based Nonlinear Classification," American Journal of Operations Research, Vol. 2 No. 3, 2012, pp. 364-373. doi: 10.4236/ajor.2012.23044.