Sparse least-squares Universum twin bounded support vector machine with adaptive Lp-norms and feature selection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10488658" target="_blank" >RIV/00216208:11320/24:10488658 - isvavai.cz</a>

Alternative codes found

RIV/44555601:13440/24:43898374

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=D_LdFwE3ym" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=D_LdFwE3ym</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2024.123378" target="_blank" >10.1016/j.eswa.2024.123378</a>

Result language

angličtina

Original language name

Sparse least-squares Universum twin bounded support vector machine with adaptive Lp-norms and feature selection

Original language description

In data analysis, when attempting to solve classification problems, we may encounter a large number of features. However, not all features are relevant for the current classification, and including irrelevant features can occasionally degrade learning performance. As a result, selecting the most relevant features is critical, especially for high-dimensional data sets in classification problems. Feature selection is an effective method for resolving this issue. It attempts to represent the original data by extracting relevant features containing useful information. In this research, our aim is to propose a p-norm least-squares Universum twin bounded support vector machine (LSp-UTBSVM) to perform classification and feature selection at the same time. Indeed, the proposed method, which outperforms the traditional least-squares Universum twin bounded support vector machine, can achieve good classification accuracy in a reasonable amount of time while also providing a sparse solution. The model we propose is an adaptive learning procedure with p-norm (0 &lt; p &lt; 1), where the parameter p can be automatically selected by the data set. The algorithm we use to find the approximate solution of this model involves solving systems of linear equations. Furthermore, we obtain new bounds for the absolute values of non-zero components of a local optimal solution. These bounds allow us to remove the zero components from an arbitrary numerical solution. Setting the parameter p, LSp-UTBSVM improves classification accuracy and selects the relevant features. Numerical experiments on a handwritten digit recognition, University of California Irvine (UCI) benchmark, Normally Distributed Clusters (NDC) and high dimensional data sets confirm the superiority of the proposed method in the accuracy of classification and the selection of relevant features in comparison with some popular methods.

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

50201 - Economic Theory

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

2024

Confidentiality

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

Name of the periodical

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Volume of the periodical

248

Issue of the periodical within the volume

Neuveden

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

123378

UT code for WoS article

001179240700001

EID of the result in the Scopus database

2-s2.0-85183984633