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

Adel, Aisha; Omar, Nazlia; Al-Shabi, Adel

doi:10.3844/jcssp.2014.2232.2239

Cited by 19 publications

(13 citation statements)

References 21 publications

(21 reference statements)

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“…The S features describing the dataset can be considered as S vectors. The similarity measure Sim(fi,fj) between any two features fi and fj can be computed as shown in equation (16). (16) As a result, the matrix in Figure represents the similarity values among the individual features.…”

Section: Proposal Of a Semantic Fusion Methods (Sf-mw) For Featuresmentioning

confidence: 99%

“…A huge number of features; in most cases; may reduce the efficiency of the adopted classifiers and also consume more time. So, the feature selection process is very important to choose a subset of high significant features and eliminate the non-significant ones [16]. Moreover, several research works were presented regarding text classification, machine learning algorithms, and feature selection methods.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Machine Learning and Feature Selection Approaches for Categorizing Arabic Text: Analysis, Comparison, and Proposal

Elnahas

El-Fishawy

Nour

et al. 2020

The Egyptian Journal of Language Engineering

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This work adopts some classification approaches for categorizing Arabic text. The approaches are operated on two datasets as test-beds. A comparative study is done to evaluate the performance of the adopted classifiers. Some feature selection methods are also analyzed, investigated, and evaluated. Selecting the most significant features is important because the huge number of features may cause performance degradation for text classification. A comparative study is done among the adopted feature selection methods for classifying Arabic documents. Moreover, a modification is done on the feature selection approaches by doing amalgamation for the chosen methods. A novel method is also proposed for selecting the most appropriate features. The method is based on the semantic fusion and multiple-words (SF-MW) for constructing the features. A comparison is done among the adopted feature selection methods and the proposed one. The experimental results show that the best performance was for the SVM classifier compared to the KNN and NB classifiers. The combination among the adopted feature selection methods presents better results compared to the individual adopted ones. The proposed feature selection method (SF-MW) is promising as it reduced the features and achieved higher classification accuracy. The accuracy improvement was about 22% for the two chosen Arabic test-beds which contain 1246 and 1500 documents respectively. The proposed method is expected to be also efficient for other Arabic and English datasets.

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Section: Proposal Of a Semantic Fusion Methods (Sf-mw) For Featuresmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Machine Learning and Feature Selection Approaches for Categorizing Arabic Text: Analysis, Comparison, and Proposal

Elnahas

El-Fishawy

Nour

et al. 2020

The Egyptian Journal of Language Engineering

View full text Add to dashboard Cite

show abstract

“…In [20], the authors studied the effect of five well-known feature selection methods: Correlation, X 2 , Information Gain [15], GSS Coefficient [21], and Relief-F [22]. Moreover, this paper proposed an approach of combination of feature selection methods based on the average weight of the features.…”

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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“…There are several techniques of machine learning in sentiment analysis they are lexicon machine learning and rule-based method. [12] Ml methods use different machine learning algorithms and labeled data to train a classifier to find sentiment. In [13] this paper deep convolution with text normalization and character level embedding, for both unstructured and structured data is conducted to perform sentiment analysis to handle the less memory space.…”

Section: II Literature Reviewmentioning

confidence: 99%

Deep Recurrent Network Based Feature Selection using Single Matrix Normalization and Eigen Vectors for Analyzing Sentiments

Hegde¹,

Hegde²

2019

IJITEE

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Sentiment analysis plays a major role in e-commerce and social media these days. Due to the increasing growth of social media, a huge number of peoples and users send their reviews through the Internet and several other sources. Analyzing this data is challenging in today's life. In this paper new normalization based feature selection method is proposed and the topic of interest here is to select the relevant features and perform the classification of the data and find the accuracy. Stability of the data is considered as the most important challenge in analyzing the sentiments. In this paper investigating the sentiments and selecting the relevant features from the data set places a major role. The aim is to work with the vector-based feature selection and check the classification performance using recurrent networks. In this paper, text mining depends on feature retrieval methods to improve accuracy and propose a single matrix normalization method to reduce the dimensions. The proposed method performs data preprocessing or sentiment classification and features reduction to improve accuracy. The proposed method achieves better accuracy than the N-gram feature selection method. The experimental results show that the proposed method has better accuracy than other traditional feature selection approaches and that the proposed method can decrease the implementation time.

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

Cited by 19 publications

References 21 publications

Machine Learning and Feature Selection Approaches for Categorizing Arabic Text: Analysis, Comparison, and Proposal

Machine Learning and Feature Selection Approaches for Categorizing Arabic Text: Analysis, Comparison, and Proposal

The Impact of Feature Selection Methods for Classifying Arabic Textual Data

Deep Recurrent Network Based Feature Selection using Single Matrix Normalization and Eigen Vectors for Analyzing Sentiments

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