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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Annals of Operations Research

ISSN

0254-5330

e-ISSN

—

Svazek periodika

2023

Číslo periodika v rámci svazku

"necislovano"

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

45

Strana od-do

"nestrankovano"

Kód UT WoS článku

000995549700002

EID výsledku v databázi Scopus

2-s2.0-85160323519