کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4635297 | 1340709 | 2007 | 14 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Data separation via a finite number of discriminant functions: A global optimization approach Data separation via a finite number of discriminant functions: A global optimization approach](/preview/png/4635297.png)
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.
Journal: Applied Mathematics and Computation - Volume 190, Issue 1, 1 July 2007, Pages 476–489