Universum parametric ? -support vector regression for binary classification problems with its applications

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F23%3A43897680" target="_blank" >RIV/44555601:13440/23:43897680 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10479-023-05369-4" target="_blank" >https://link.springer.com/article/10.1007/s10479-023-05369-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10479-023-05369-4" target="_blank" >10.1007/s10479-023-05369-4</a>

Result language

angličtina

Original language name

Universum parametric ? -support vector regression for binary classification problems with its applications

Original language description

Universum data sets, a collection of data sets that do not belong to any specific class in a classification problem, give previous information about data in the mathematical problem under consideration to enhance the classifiers? generalization performance. Recently, some researchers have integrated Universum data into the existing models to propose new models which result in improved classification performance. Inspired by these Universum models, an efficient parametric ? -support vector regression with Universum data (U Par- ? -SVR) is proposed in this work. This method, which finds two non-parallel hyperplanes by solving one optimization problem and considers heteroscedastic noise, overcomes some common disadvantages of the previous methods. The U Par- ? -SVR includes unlabeled samples that don?t belong to any class in the training process, which results in a quadratic programming problem. Two approaches are proposed to solve this problem. The first approach derives the dual formulation using the Lagrangian function and KKT conditions. Furthermore, a least squares parametric ? -support vector regression with Universum data (named LS- U Par- ? -SVR) is suggested to further increase the generalization performance. The LS- U Par- ? -SVR solves a single system of linear equations, instead of addressing a quadratic programming problem in the dual formulation. Numerical experiments on artificial, UCI, credit card, NDC, and handwritten digit recognition data sets show that the suggested Universum model and its solving methodologies improve prediction accuracy.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Annals of Operations Research

ISSN

0254-5330

e-ISSN

—

Volume of the periodical

2023

Issue of the periodical within the volume

"necislovano"

Country of publishing house

CH - SWITZERLAND

Number of pages

45

Pages from-to

"nestrankovano"

UT code for WoS article

000995549700002

EID of the result in the Scopus database

2-s2.0-85160323519