Arabic Text Classification using K-NN and Naive Bayes

Bawaneh, Mohammed J.; Alkoffash, Mahmud S.; Rabea, Adnan I. Al

doi:10.3844/jcssp.2008.600.605

Cited by 29 publications

(20 citation statements)

References 10 publications

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“…Recently, due to the massive amount of documents that need to be classified, the automatic approaches have been more widely used. Naïve Bayes and SVM achieved the best results, especially in the text classification (Al-Saleem, 2010;Bawaneh et al, 2008;El-Halees, 2008;El Kourdi et al, 2004;Kanaan et al, 2009;Khorsheed and Al-Thubaity, 2013;Thabtah et al, 2009).…”

Section: Classification Phasementioning

confidence: 99%

“…There are limited amount of research, however, that have tried to improve Text Classification (TC) processes using AWN components, such as using n-grams, synonym and concepts (Alahmadi et al, 2014;Elberrichi and Abidi, 2012). Many attempts have focused on using various classification algorithms to improve Arabic text classification (Al-Saleem, 2010;Bawaneh et al, 2008;El-Halees, 2008;Kanaan et al, 2009). All existing Arabic classification methods are not comparable to human classification since most of them do not consider text semantics.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

Yousif¹,

Samawi²,

Elkabani³

et al. 2015

Journal of Computer Science

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Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information (written in Arabic language) on the web. In most text classification processes, feature selection is crucial task since it highly affects the classification accuracy. Generally, two types of features could be used: Statistical based features and semantic and concept features. The main interest of this paper is to specify the most effective semantic and concept features on Arabic text classification process. In this study, two novel features that use lexical, semantic and lexico-semantic relations of Arabic WordNet (AWN) ontology are suggested. The first feature set is List of Pertinent Synsets (LoPS), which is list of synsets that have a specific relation with the original terms. The second feature set is List of Pertinent Words (LoPW), which is list of words that have a specific relation with the original terms. Fifteen different relations (defined in AWN ontology) are used with both proposed features. Naïve Bayes classifier is used to perform the classification process. The experimental results, which are conducted on BBC Arabic dataset, show that using LoPS feature set improves the accuracy of Arabic text classification compared with the well-known Bag-of-Word feature and the recent Bag-of-Concept (synset) features. Also, it was found that LoPW (especially with related-to relation) improves the classification accuracy compared with LoPS, Bagof-Word and Bag-of-Concept.

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Section: Classification Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement of Arabic Text Classification Using Semantic Relations of Arabic WordNet

Yousif¹,

Samawi²,

Elkabani³

et al. 2015

Journal of Computer Science

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show abstract

“…In [24], the authors applied kNN and NB on Arabic text and concluded that kNN has better performance than NB; they also concluded that feature selection, the size of training set, and the value of k affect the performance of classification. The researchers also posed the problem of unavailability of freely accessible Arabic corpus.…”

Section: Related Workmentioning

confidence: 99%

Combining Different Approaches to Improve Arabic Text Documents Classification

Abuhaiba¹,

Dawoud²

2017

IJISA

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Abstract-The objective of this research is to improve Arabic text documents classification by combining different classification algorithms. To achieve this objective we build four models using different combination methods.The first combined model is built using fixed combination rules, where five rules are used; and for each rule we used different number of classifiers. The best classification accuracy, 95.3%, is achieved using majority voting rule with seven classifiers, and the time required to build the model is 836 seconds.The second combination approach is stacking, which consists of two stages of classification. The first stage is performed by base classifiers, and the second by a meta classifier. In our experiments, we used different numbers of base classifiers and two different meta classifiers: Naï ve Bayes and linear regression. Stacking achieved a very high classification accuracy, 99.2% and 99.4%, using Naï ve Bayes and linear regression as meta classifiers, respectively. Stacking needed a long time to build the models, which is 1963 seconds using naï ve Bayes and 3718 seconds using linear regression, since it consists of two stages of learning.The third model uses AdaBoost to boost a C4.5 classifier with different number of iterations. Boosting improves the classification accuracy of the C4.5 classifier; 95.3%, using 5 iterations, and needs 1175 seconds to build the model, while the accuracy is 99.5% using 10 iterations and requires 1966 seconds to build the model.The fourth model uses bagging with decision tree. The accuracy is 93.7% achieved in 296 seconds when using 5 iterations, and 99.4% when using 10 iteration requiring 471 seconds. We used three datasets to test the combined models: BBC Arabic, CNN Arabic, and OSAC datasets. The experiments are performed using Weka and RapidMiner data mining tools. We used a platform of Intel Core i3 of 2.2 GHz CPU with 4GB RAM.The results of all models showed that combining classifiers can effectively improve the accuracy of Arabic text documents classification.

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“…Bawaneh et al [8] compared between the two classifiers KNearest Neighbor (KNN) and Naïve Bayesian (NB). The light stemmer was used as feature and TFIDF (Term FrequencyInverse Document Frequency) measurement as a method of weighting features.…”

Section: Related Workmentioning

confidence: 99%