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A new method of linear support vector regression with interval data | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 857-868 اصل مقاله (457.28 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2020.19163.2067 | ||
| نویسندگان | ||
| Mojtaba Baymani* 1؛ Hoda Saffaran2؛ Nima Salehi-M.3 | ||
| 1Applied Mathematics, Mathematics, Quchan University of Technology, Quchan, Iran | ||
| 2Department of Computer, Engineering Faculty, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| 3Department of Computer, Engineering Faculty, Ferdowsi University of Mashhad, Mashhad, IRAN | ||
| تاریخ دریافت: 11 آذر 1398، تاریخ بازنگری: 21 دی 1398، تاریخ پذیرش: 09 بهمن 1398 | ||
| چکیده | ||
| In this paper, the linear support vector regression approach is proposed for solving the regression problem with interval data, which is called interval support vector regression(ISVR). The ISVR approach is equivalent to solving a linear constrained quadratic programming problem (QPP) with an interval cost coefficient in which the value of the objective function is in an interval. Instead of solving an interval QPP, we solve two QPPs and prove that the cost values of these two problems are the lower bound and the upper bound of the target value of the interval QPP. We show these two mentioned QPPs are equivalent to two support vector regression problems which the first problem applies the lower bound of data and the second problem considers the upper bound of the data. to obtain the regression function. Some experiments are made to demonstrate the performance of our method compared with the known algorithms on several artificial, benchmark and real practical datasets. | ||
| کلیدواژه‌ها | ||
| Quadratic programming؛ Computing methodologies and applications؛ Linear regression. | ||
| مراجع | ||
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