Text Classification for Arabic Words Using Rep-Tree

Naji, Hamza; Ashour, Wesam M.

doi:10.5121/ijcsit.2016.8208

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…It follows the principle of computing the information gain with entropy. Also, this principle is applied to minimize the error results between the actual and the desired output, which contributes in reaching a high accuracy [59]. Another high-performance model that belongs to the tree category is M5P.…”

Section: Discussionmentioning

confidence: 99%

Current Techniques for Diabetes Prediction: Review and Case Study

et al. 2019

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Diabetes is one of the most common diseases worldwide. Many Machine Learning (ML) techniques have been utilized in predicting diabetes in the last couple of years. The increasing complexity of this problem has inspired researchers to explore the robust set of Deep Learning (DL) algorithms. The highest accuracy achieved so far was 95.1% by a combined model CNN-LSTM. Even though numerous ML algorithms were used in solving this problem, there are a set of classifiers that are rarely used or even not used at all in this problem, so it is of interest to determine the performance of these classifiers in predicting diabetes. Moreover, there is no recent survey that has reviewed and compared the performance of all the proposed ML and DL techniques in addition to combined models. This article surveyed all the ML and DL techniques-based diabetes predictions published in the last six years. In addition, one study was developed that aimed to implement those rarely and not used ML classifiers on the Pima Indian Dataset to analyze their performance. The classifiers obtained an accuracy of 68%–74%. The recommendation is to use these classifiers in diabetes prediction and enhance them by developing combined models.

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Section: Discussionmentioning

confidence: 99%

Current Techniques for Diabetes Prediction: Review and Case Study

et al. 2019

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“…A. Alhaj et al, 2019;El Mahdaouy et al, 2016;Gharbat et al, 2019;Hijazi et al, 2022;Muaad et al, 2022;Naji et al, 2017), topic segmentation (Alahmadi et al, 2022;Naili et al, 2018), andsummarization (AlOudah et al, 2019;Etaiwi & Awajan, 2022;Tanfouri & Jarray, 2022).…”

Section: Mentionmentioning

confidence: 99%

Tashaphyne: A Python package for Arabic Light Stemming

Zerrouki

2024

JOSS

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Stemming is an important task in natural language processing that involves reducing a word to its root form, or stem. In many cases, stemming can significantly improve the accuracy and efficiency of text analysis tasks such as information retrieval, text classification, and sentiment analysis. For the Arabic language, which has a rich morphology with a large number of prefixes and suffixes, stemming is particularly challenging. Tashaphyne provides an effective solution to this challenge, making it a valuable tool for researchers and practitioners working with Arabic text data.

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“…In [30] centered around the distributed researches in the field of Arabic text classification and illustrates a logical view its procedure and camper the assessment of text classification strategies that were utilized. The authors in [12] gave a novel framework for text classification dependent on BPSO/REP-Tree hybrid. The first term refers to the -Binary Particle Swarm Optimization‖ that we use it for the feature selection process and the second term refers the classifier we used -Reduced Error Pruning Tree‖.…”

Section: Previous Workmentioning

confidence: 99%

“…The explosion of Internet contents in various languages including Arabic presented a pressing need for effective classification. Classification algorithms are developed for this sole purpose which are called Classifier) [12] [13].…”

Section: Introductionmentioning

confidence: 99%

A Closer Look at Arabic Text Classification

Abdeen¹,

Albouq²,

Elmahalawy³

et al. 2019

IJACSA

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The world has witnessed an information explosion in the past two decades. Electronic devices are now available in many varieties such as PCs, Laptops, book readers, mobile devices and with relatively affordable prices. This and the ubiquitous use of software applications such as social media and cloud applications, and the increasing trend towards digitalization, the amount of information on the global cloud has surged to an unprecedented level. Therefore, a dire need exists in order to mine this massively large amount of data and produce meaningful information. Text Classification is one of the known and well established data mining techniques that has been used and reported in the literature. Text classification methods include statistical and machine learning algorithms such as Naive Baysian, Support Vector Machines and others have widely been used. Many works have been reported regarding text classification of various languages including English, Chinese, Russian, and many others. Arabic is the fifth most spoken language in the world. There has been many works in the literature for Arabic text classification. However, and to the best of our knowledge, there is no recent work that presents a good, critical and comprehensive survey of the Arabic text classification for the past two decades. The aim of this paper is to present a concise and yet comprehensive review of the Arabic text classification. We have covered over 50 research papers covering the past two decades (2000 -2019). The main focus of this paper is to address the following issues: 1) The techniques reported in the literature including. 2) New Techniques. 3) Most claimed efficient technique. 4) Datasets used and which ones are most popular. 5) Which feature selection techniques are used? 6) Popular classes/categories used. 7) Effect of stemming techniques on classification results.

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Text Classification for Arabic Words Using Rep-Tree

Abstract: ABSTRACT

Cited by 8 publications

References 9 publications

Current Techniques for Diabetes Prediction: Review and Case Study

Current Techniques for Diabetes Prediction: Review and Case Study

Tashaphyne: A Python package for Arabic Light Stemming

A Closer Look at Arabic Text Classification

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