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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Vydáno v:Future internet Ročník 14; číslo 7; s. 194
Hlavní autoři: Alhaj, Yousif A., Dahou, Abdelghani, Al-qaness, Mohammed A. A., Abualigah, Laith, Abbasi, Aaqif Afzaal, Almaweri, Nasser Ahmed Obad, Elaziz, Mohamed Abd, Damaševičius, Robertas 
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2022
Témata:
Algorithms
Arabic language
Classification
Classifiers
Configurations
Datasets
Documents
Feature extraction
Feature selection
Internet
Literature reviews
Machine learning
Methods
Optimization techniques
Particle swarm optimization
Performance enhancement
Performance evaluation
Support vector machines
Text categorization
text classification
ISSN:1999-5903, 1999-5903
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Shrnutí: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 alongside the classifier. Although several text classification methods have been proposed for the Arabic language using different techniques, such as feature selection methods, an ensemble of classifiers, and discriminative features, choosing the optimal method becomes an NP-hard problem considering the huge search space. Therefore, we propose a method, called Optimal Configuration Determination for Arabic text Classification (OCATC), which utilized the Particle Swarm Optimization (PSO) algorithm to find the optimal solution (configuration) from this space. The proposed OCATC method extracts and converts the features from the textual documents into a numerical vector using the Term Frequency-Inverse Document Frequency (TF–IDF) approach. Finally, the PSO selects the best architecture from a set of classifiers to feature selection methods with an optimal number of features. Extensive experiments were carried out to evaluate the performance of the OCATC method using six datasets, including five publicly available datasets and our proposed dataset. The results obtained demonstrate the superiority of OCATC over individual classifiers and other state-of-the-art methods.
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ISSN:1999-5903
1999-5903
DOI:10.3390/fi14070194