Sparse least squares K-SVCR multi-class classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F24%3A43898581" target="_blank" >RIV/44555601:13440/24:43898581 - isvavai.cz</a>

Výsledek na webu

<a href="https://jnva.biemdas.com/archives/2507" target="_blank" >https://jnva.biemdas.com/archives/2507</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23952/jnva.8.2024.6.07" target="_blank" >10.23952/jnva.8.2024.6.07</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sparse least squares K-SVCR multi-class classification

Popis výsledku v původním jazyce

This paper introduces a novel model, the sparse least squares K-class support vector classificationregression with adaptive p-norm (PLSTKSVC), to tackle challenges in multi-class classification. Leveraging a &quot;1-versus-1-versus-rest&quot; structure, PLSTKSVC dynamically adjusts the parameter p based on the data, enabling an adaptive learning framework. By incorporating cardinality-constrained optimization, the model seamlessly integrates feature selection and classification. Although the p-norm is non-convex for 0 &lt; p&lt;1, PLSTKSVC efficiently addresses the associated optimization via linear systems of equations. PLSTKSVC offers several advantages, including simultaneous feature selection and classification, robust theoretical foundations, algorithmic efficiency, and strong empirical validation. The model?s theoretical contributions include lower bounds on non-zero solution entries and upper bounds on the optimal solution norm. Experimental results on multi-class classification datasets highlight the superior performance of PLSTKSVC, establishing it as a significant advancement in machine learning.

Název v anglickém jazyce

Sparse least squares K-SVCR multi-class classification

Popis výsledku anglicky

This paper introduces a novel model, the sparse least squares K-class support vector classificationregression with adaptive p-norm (PLSTKSVC), to tackle challenges in multi-class classification. Leveraging a &quot;1-versus-1-versus-rest&quot; structure, PLSTKSVC dynamically adjusts the parameter p based on the data, enabling an adaptive learning framework. By incorporating cardinality-constrained optimization, the model seamlessly integrates feature selection and classification. Although the p-norm is non-convex for 0 &lt; p&lt;1, PLSTKSVC efficiently addresses the associated optimization via linear systems of equations. PLSTKSVC offers several advantages, including simultaneous feature selection and classification, robust theoretical foundations, algorithmic efficiency, and strong empirical validation. The model?s theoretical contributions include lower bounds on non-zero solution entries and upper bounds on the optimal solution norm. Experimental results on multi-class classification datasets highlight the superior performance of PLSTKSVC, establishing it as a significant advancement in machine learning.

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

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Nonlinear and Variational Analysis

ISSN

2560-6921

e-ISSN

2560-6778

Svazek periodika

8

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CA - Kanada

Počet stran výsledku

19

Strana od-do

953-971

Kód UT WoS článku

001374806800007

EID výsledku v databázi Scopus

2-s2.0-85208017159