Feature subset selection for Arabic document categorization using BPSO-KNN

Chantar, Hamouda; Corne, David

doi:10.1109/nabic.2011.6089647

Cited by 48 publications

(42 citation statements)

References 7 publications

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“…In [5], the authors proposed a hybrid approach, namely Binary PSO-KNN that select the best subset of the relevant features. Also, demonstrated the proposed approach with three known machine learning algorithms: Support Vector Machines (SVMs), NB, and J48 Decision Tree.…”

Section: Related Workmentioning

confidence: 99%

The Impact of Feature Selection Methods for Classifying Arabic Textual Data

Issa*,

Abu-Arqoub*,

Hadi

2019

IJRTE

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Text classification is a vital process due to the large volume of electronic articles. One of the drawbacks of text classification is the high dimensionality of feature space. Scholars developed several algorithms to choose relevant features from article text such as Chi-square (x2 ), Information Gain (IG), and Correlation (CFS). These algorithms have been investigated widely for English text, while studies for Arabic text are still limited. In this paper, we investigated four well-known algorithms: Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree against benchmark Arabic textual datasets, called Saudi Press Agency (SPA) to evaluate the impact of feature selection methods. Using the WEKA tool, we have experimented the application of the four mentioned classification algorithms with and without feature selection algorithms. The results provided clear evidence that the three feature selection methods often improves classification accuracy by eliminating irrelevant features.

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Section: Related Workmentioning

confidence: 99%

The Impact of Feature Selection Methods for Classifying Arabic Textual Data

Issa*,

Abu-Arqoub*,

Hadi

2019

IJRTE

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“…Reference [8] demonstrated a combination of Binary Particle Swarm Optimization and KNN as a feature selection criterion in classifying three Arabic corpora: "Al-jazeera-News", "Akhbar-Alkhaleej" and "Alwatan Arabic". The authors used three classification algorithms: J48 Decision Tree, SVM and NB.…”

Section: Related Workmentioning

confidence: 99%

“…In each class, 80% of the texts are used for training and 20% for testing. Many researches in Arabic TC have adopted this dataset using different algorithms [49], [20], [8], [13], [28].…”

Section: The Datasetmentioning

confidence: 99%

“…Some FS criteria that were used in Arabic TC research include Chi Square (CHI) [13]. [24]- [26], [55], [9], [7], Cross Validation [2], Document Frequency (DF) [24], [20], Information Gain(IG) [24], [20], [5], Correlation Coefficient (CC) [20], Semi-Automatic Categorization Method (SACM), Mutual Information (MI) [20], BPSO-KNN (Binary Particle Swarm Optimization-K-Nearest-Neighbor ) [8] and ACM (Automatic Categorization Method) [56]. Some researchers performed random FS [18]and others haven't used any FS [11].…”

Section: Feature Selection (Fs)mentioning

confidence: 99%

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Polynomial Neural Networks versus Other Arabic Text Classifiers

Al-Tahrawi¹

2016

JSW

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Many Text Classification (TC) algorithms have been proposed for Arabic TC. Polynomial Neural Networks (PNNs) were used recently in English TC, and have proved to be competitive to the state of the art text classifiers in this field. Lately, they were proposed for classifying Arabic documents. In this research paper, an experimental study that directly compares PNNs against five famous classification algorithms in TC is conducted on Aljazeera-News Arabic dataset. All experiments use the same TC settings, like preprocessing, Feature Selection (FS) and reduction criteria, feature weighting and classifier performance evaluation measures. These algorithms are: SVM (Support Vector Machines), NB (Naive Bayes), kNN (k-Nearest-Neighbor), LR (Logistic Regression) and RBF (Radial Basis Function networks). Results reached in this study reveal that PNN are competitive classifiers in the field of Arabic TC.

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“…The results of the experiments have revealed that, the proposed term weighting approaches improved the classification task. Chen et al (2009) Unlike English language, a limited number of researches had been done for Arabic (Al-Salemi and Ab Aziz, 2010;Chantar and Corne 2011;Hawashin et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Combined Feature Selection Methods for Arabic Text Classification

Adel¹,

Omar²,

Al-Shabi³

2014

Journal of Computer Science

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Text classification is a very important task due to the huge amount of electronic documents. One of the problems of text classification is the high dimensionality of feature space. Researchers proposed many algorithms to select related features from text. These algorithms have been studied extensively for English text, while studies for Arabic are still limited. This study introduces an investigation on the performance of five widely used feature selection methods namely Chi-square, Correlation, GSS Coefficient, Information Gain and Relief F. In addition, this study also introduces an approach of combination of feature selection methods based on the average weight of the features. The experiments are conducted using Naïve Bayes and Support Vector Machine classifiers to classify a published Arabic corpus. The results show that the best results were obtained when using Information Gain method. The results also show that the combination of multiple feature selection methods outperforms the best results obtain by the individual methods.

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Feature subset selection for Arabic document categorization using BPSO-KNN

Cited by 48 publications

References 7 publications

The Impact of Feature Selection Methods for Classifying Arabic Textual Data

The Impact of Feature Selection Methods for Classifying Arabic Textual Data

Polynomial Neural Networks versus Other Arabic Text Classifiers

A Comparative Study of Combined Feature Selection Methods for Arabic Text Classification

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