On Regularization Based Twin Support Vector Regression with Huber Loss

Abstract

Twin support vector regression (TSVR) is generally employed with ε-insensitive loss function which is not well capable to handle the noises and outliers. According to the definition, Huber loss function performs as quadratic for small errors and linear for others and shows better performance in comparison to Gaussian loss hence it restrains easily for a different type of noises and outliers. Recently, TSVR with Huber loss (HN-TSVR) has been suggested to handle the noise and outliers. Like TSVR, it is also having the singularity problem which degrades the performance of the model. In this paper, regularized version of HN-TSVR is proposed as regularization based twin support vector regression (RHN-TSVR) to avoid the singularity problem of HN-TSVR by applying the structured risk minimization principle that leads to our model convex and well-posed. This proposed RHN-TSVR model is well capable to handle the noise as well as outliers and avoids the singularity issue. To show the validity and applicability of proposed RHN-TSVR, various experiments perform on several artificial generated datasets having uniform, Gaussian and Laplacian noise as well as on benchmark different real-world datasets and compare with support vector regression, TSVR, ε-asymmetric Huber SVR, ε-support vector quantile regression and HN-TSVR. Here, all benchmark real-world datasets are embedded with a different significant level of noise 0%, 5% and 10% on different reported algorithms with the proposed approach. The proposed algorithm RHN-TSVR is showing better prediction ability on artificial datasets as well as real-world datasets with a different significant level of noise compared to other reported models.

Construction and Application of VS-DBN Anti-Theft Diagnosis Model Based on Neural Architecture Search

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Development Status and Trend Analysis of Internet of Medical Things Industry in China

“…Digitization is the foundation of IoT, including the procurement of medical laboratory equipment, reagents and consumables, technical outsourcing service customization, to intermediate links such as consultation, appointment, diagnosis, treatment, medication and rehabilitation, to the delivery of final products, services, and medical insurance [14]. At present, the degree of digitalization of the supply-side and demand-side enterprise management of medical elements is relatively low, which is a key transformation link of the medical industry chain [15][16]. The industrial Internet of Things platform will be an important driver of digitalization, and gradually realize the digitalization of procurement, digitalization of production, digitalization of management, digitalization of marketing, and transactions.…”

Forecasting peak electric load: Robust support vector regression with smooth nonconvex ϵ‐insensitive loss

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