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مقالات فارسیISIکنفرانسهاژورنالها

Regression Analysis Using Core Vector Machine Technique Based on Kernel Function Optimization

محل انتشار: مجله سیستم های دینامیکی کاربردی و کنترل، دوره: 6، شماره: 3
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 57

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استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:
https://civilica.com/doc/1875276

شناسه ملی سند علمی:

JR_JADSC-6-3_001

تاریخ نمایه سازی: 13 دی 1402

چکیده مقاله:

Core vector regression (CVR) is an extension of the core vector machine algorithm for regression estimation by generalizing the minimum bounding ball (MEB) problem. As an estimator, both the kernel function and its parameters can significantly affect the prediction precision of CVR. In this paper, a method to improve CVR performance using pre-processing based on data feature extraction and Grid algorithm is proposed to obtain appropriate parameters values of the main formulation and its kernel function. The CVR estimated mean absolute error rate here is the evaluation criterion of the proposed method that should be minimized. In addition, some benchmark datasets out of different databases were used to evaluate the proposed parameter optimization approach. The obtained numerical results show that the proposed method can reduce the CVR error with an acceptable time and space complexity. Therefore, it is able to deal with very large data and real world regression problems.

کلیدواژه ها:

Core vector regression ، Kernel function ، Grid algorithm ، Parameter selection

نویسندگان

Babak Afshin

Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran‎

Mohammad Ebrahim Shiri

Department of Mathematics and Computer Sciences, Amirkabir University of Technology, Tehran, Iran

Kamran Layeghi

Department of Computer Engineering, North Tehran Branch, ‎Islamic Azad University, Tehran, Iran‎

Hamid HajSeyyedJavadi

Department of Mathematics and Computer Sciences, Shahed ‎University, Tehran, Iran

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