Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257763" target="_blank" >RIV/61989100:27240/25:10257763 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s11227-024-06606-8#author-information" target="_blank" >https://link.springer.com/article/10.1007/s11227-024-06606-8#author-information</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11227-024-06606-8" target="_blank" >10.1007/s11227-024-06606-8</a>

Result language

angličtina

Original language name

Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems

Original language description

Feature selection is one of the important steps in data mining to reduce the dimensions of datasets. Due to the fact that feature selection is inherently a NP-hard problem, no deterministic algorithm has been identified to solve this problem in acceptable time. Meta-heuristic algorithms are reliable alternatives to solve such problems in acceptable time. In the literature, a large number of algorithms have been proposed to solve the feature selection problem using meta-heuristic optimization algorithms. In this work, a new feature selection algorithm based on ARO meta-heuristic algorithm and DBSCAN clustering algorithm with automatic adjustment of input parameters (ARO-DBSCAN) is proposed. Using side algorithms to improve the performance of meta-heuristic algorithms can potentially cause getting stuck in local optima. The method proposed in the work has improved the performance of the ARO meta-heuristic for the feature selection problem without increasing the probability stuck in local optima. The use of DBSCAN clustering algorithm, which is based on density, increases the exploitation of ARO in the search space while maintaining its exploration. As a result, the performance of the ARO algorithm increases significantly in feature selection problems. The proposed algorithm is compared with 8 state-of-the-art feature selection algorithms on the UCI benchmark datasets and three real-world high-dimensional datasets. Result of experiments show the better performance of ARO-DBSCAN algorithm in the appropriate execution time. Also, in high-dimensional data, the proposed method is able to significantly reduce the number of dataset features. which makes the analysis of these datasets more efficient. The source code for the algorithm being proposed is accessible to the public on https://github.com/alihamdipour/ARO-DBSCAN.

Czech name

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Czech description

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Type

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

CEP classification

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OECD FORD branch

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

Project

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Continuities

O - Projekt operacniho programu

Publication year

2025

Confidentiality

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

Name of the periodical

Journal of Supercomputing

ISSN

0920-8542

e-ISSN

1573-0484

Volume of the periodical

81

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

34

Pages from-to

nestránkováno

UT code for WoS article

001351503500001

EID of the result in the Scopus database

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