Data separation via a finite number of discriminant functions: A global optimization approach
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摘要
This paper presents a mixed 0–1 integer and linear programming (MILP) model for separation of data via a finite number of non-linear and non-convex discriminant functions. The MILP model concurrently optimizes the parameters of the user-provided individual discriminant functions to implement a decision boundary for an optimal separation of data under analysis.The performance of the MILP-based classification of data is illustrated on randomly generated two dimensional datasets and extensively tested on six well-studied datasets in data mining research, in comparison with three well-established supervised learning methodologies, namely, the multisurface method, the logical analysis of data and the support vector machines. Numerical results from these experiments show that the new MILP-based classification of data is an effective and useful methodology for supervised learning.
论文关键词:Data classification,Supervised learning,Mixed 0–1 and linear programming,Global optimization
论文评审过程:Available online 1 February 2007.
论文官网地址:https://doi.org/10.1016/j.amc.2007.01.051