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

Result code in 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>

Alternative codes found

RIV/44555601:13440/23:43897767

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/GA22-11117S" target="_blank" >GA22-11117S: Global sensitivity analysis and stability in optimization problems</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Name of the periodical

Computers and Operations Research

ISSN

0305-0548

e-ISSN

1873-765X

Volume of the periodical

160

Issue of the periodical within the volume

Neuveden

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

106370

UT code for WoS article

001055149700001

EID of the result in the Scopus database

2-s2.0-85167836230