A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of features alongs...

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Bibliographic Details
Main Authors: Abbasi, A.A (Author), Abualigah, L. (Author), Alhaj, Y.A (Author), Almaweri, N.A.O (Author), Al-Qaness, M.A.A (Author), Dahou, A. (Author), Damaševičius, R. (Author), Elaziz, M.A (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Arabic texts
Classification (of information)
Computational complexity
Configuration determination
feature extraction
feature selection
Feature Selection
Feature selection methods
Features extraction
Features selection
Inverse problems
Learning systems
Numerical methods
Particle swarm
particle swarm optimization
Particle swarm optimization
Particle swarm optimization (PSO)
Swarm optimization
text classification
Text classification
Text classification methods
Text processing
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