English Text Classification Using Improved Recursive Feature Elimination (IRFE) Algorithm

تصنيف النص الإنجليزي باستخدام الخوارزمية العودية المحسنة لإزالة الخواص (IRFE)

المؤلفون

  • Esraa H. Abd Al-Ameer, Ahmed H. Aliwy

الكلمات المفتاحية:

Decision Tree
شجرة القرار
Naïve Bayes
نايف بايز
Text Classification
تصنيف النص
features selection
اختيار الخواص
(IRFE)
الخوارزمية العودية

الملخص

Documents classification is from most important fields for Natural language processing and text mining. There are many algorithms can be used for this task. In this paper, focuses on improving Text Classification by feature selection. This means determine some of the original features without affecting the accuracy of the work, where our work is a new feature selection method was suggested which can be a general formulation and mathematical model of Recursive Feature Elimination (RFE). The used method was compared with other two well-known feature selection methods: Chi-square and threshold. The results proved that the new method is comparable with the other methods, The best results were 83% when 60% of features used, 82% when 40% of features used, and 82% when 20% of features used. The tests were done with the Naïve Bayes (NB) and decision tree (DT) classification algorithms , where the used dataset is a well-known English data set “20 newsgroups text” consists of approximately 18846 files. The results showed that our suggested feature selection method is comparable with standard Like Chi-square.

السيرة الشخصية للمؤلف

Esraa H. Abd Al-Ameer, Ahmed H. Aliwy

 

Faculty Education for Girls || University of Kufa || Iraq

التنزيلات

منشور

2020-06-30

كيفية الاقتباس

1.
Esraa H. Abd Al-Ameer, Ahmed H. Aliwy. English Text Classification Using Improved Recursive Feature Elimination (IRFE) Algorithm: تصنيف النص الإنجليزي باستخدام الخوارزمية العودية المحسنة لإزالة الخواص (IRFE). jesit [انترنت]. 30 يونيو، 2020 [وثق 7 ديسمبر، 2022];4(2):120-1. موجود في: https://journals.ajsrp.com/index.php/jesit/article/view/2642

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