Newton-based approach to solving K-SVCR and Twin-KSVC multi-class classification in the primal space

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10472183" target="_blank" >RIV/00216208:11320/23:10472183 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/44555601:13440/23:43897767

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6Niq2fXsdM" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6Niq2fXsdM</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cor.2023.106370" target="_blank" >10.1016/j.cor.2023.106370</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Newton-based approach to solving K-SVCR and Twin-KSVC multi-class classification in the primal space

Popis výsledku v původním jazyce

Multi-class classification is an important problem in machine learning, which often occurs in the real world and is an ongoing research issue. Support vector classification-regression machine for -class classification (K-SVCR) and twin -class support vector classification (Twin-KSVC) are two novel machine learning methods for multi-class classification problems. This paper presents novel methods to solve the primal problems of K-SVCR and Twin-KSVC, known as NK-SVCR and NTW-KSVC, respectively. The proposed methods evaluate all training data into a &quot;1-versus-1-versus-rest&quot;structure, so it generates ternary outputs {-1,0, +1}. The primal problems are reformulated as unconstrained optimization problems so that the objective functions are only once differentiable, not twice, therefore an extension of the Newton-Armijo algorithm is adopted for finding their solution. To test the efficiency and validity of the proposed methods, we compare the classification accuracy and learning time of these methods with K-SVCR and Twin-KSVC on the United States Postal Service (USPS) handwriting digital data sets and several University of California Irvine (UCI) benchmark data sets. To analyze more in aspect of training time, we also compared all methods on the multi-class version of the Normally Distributed Clustered (NDC) database. To further analyze classification accuracy and learning time differences between the classifiers, the statistical Friedman&apos;s test is used.

Název v anglickém jazyce

Newton-based approach to solving K-SVCR and Twin-KSVC multi-class classification in the primal space

Popis výsledku anglicky

Multi-class classification is an important problem in machine learning, which often occurs in the real world and is an ongoing research issue. Support vector classification-regression machine for -class classification (K-SVCR) and twin -class support vector classification (Twin-KSVC) are two novel machine learning methods for multi-class classification problems. This paper presents novel methods to solve the primal problems of K-SVCR and Twin-KSVC, known as NK-SVCR and NTW-KSVC, respectively. The proposed methods evaluate all training data into a &quot;1-versus-1-versus-rest&quot;structure, so it generates ternary outputs {-1,0, +1}. The primal problems are reformulated as unconstrained optimization problems so that the objective functions are only once differentiable, not twice, therefore an extension of the Newton-Armijo algorithm is adopted for finding their solution. To test the efficiency and validity of the proposed methods, we compare the classification accuracy and learning time of these methods with K-SVCR and Twin-KSVC on the United States Postal Service (USPS) handwriting digital data sets and several University of California Irvine (UCI) benchmark data sets. To analyze more in aspect of training time, we also compared all methods on the multi-class version of the Normally Distributed Clustered (NDC) database. To further analyze classification accuracy and learning time differences between the classifiers, the statistical Friedman&apos;s test is used.

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

<a href="/cs/project/GA22-11117S" target="_blank" >GA22-11117S: Globální analýza citlivosti a stabilita v optimalizačních úlohách</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Computers and Operations Research

ISSN

0305-0548

e-ISSN

1873-765X

Svazek periodika

160

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

106370

Kód UT WoS článku

001055149700001

EID výsledku v databázi Scopus

2-s2.0-85167836230