Binary Archimedes Optimization Algorithm based Feature Selection for Regression Problem

The use of datasets became paramount in many searches in one hand, on the other hand the rapidly growth of data size involves computational complexity and reduces model performances, this encourage us to find new methods to deal with this problem. Features Selection is the one of the main task used...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) S. 1 - 7
Hauptverfasser: Amine, Djermane, Hichem, Haouassi, Soumia, Zertal
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 12.10.2022
Schlagworte:
Archimedes optimization algorithm
Classification algorithms
Data mining
Data models
Feature extraction
feature selection
Metaheuristics
Prediction algorithms
Predictive models
regression
Search problems
swarm-based optimization
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The use of datasets became paramount in many searches in one hand, on the other hand the rapidly growth of data size involves computational complexity and reduces model performances, this encourage us to find new methods to deal with this problem. Features Selection is the one of the main task used to resolve this issue. In this paper we propose a novel features selection method for regression task based on AOA (Archimedes Optimization Algorithm), experimental results shows that the proposed method can efficiently reduce dataset size and improve model performance.
DOI:10.1109/PAIS56586.2022.9946903