Performance of NB and SVM Classifiers in Arabic Text Data

被引：0
|
作者：
Eljinini, Mohammad Ali H. [1 ]
Hadi, Wa'el Musa
Mohammad, Adel Hamdan
Ghatasheh, Mohammad [1 ]
机构：
[1] AL Isra Univ, Amman, Jordan
来源：
BUSINESS TRANSFORMATION THROUGH INNOVATION AND KNOWLEDGE MANAGEMENT: AN ACADEMIC PERSPECTIVE, VOLS 3 AND 4 | 2010年
关键词：
Text Categorization; Naive Bayesian; Arabic Text Data; Support Vector Machine;
D O I：
暂无
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
Text classification is a supervised learning technique that uses labelled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. This paper investigates Naive Bayesian method (NB) and Support Vector Machine (SVM) on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorization data sets reveal that NB algorithm outperforms the SVM with regards to F1 measure.
引用
收藏
页码：2593 / 2599
页数：7
相关论文
共 50 条
  • [1] Performance of KNN and SVM classifiers on full word Arabic articles
    Hmeidi, Ismail
    Hawashin, Bilal
    El-Qawasmeh, Eyas
    [J]. ADVANCED ENGINEERING INFORMATICS, 2008, 22 (01) : 106 - 111
  • [2] Improved Generalization of Arabic Text Classifiers
    Khaddaj, Alaa
    Hajj, Hazem
    El-Hajj, Wassim
    [J]. FOURTH ARABIC NATURAL LANGUAGE PROCESSING WORKSHOP (WANLP 2019), 2019, : 167 - 174
  • [3] Machine Reading of Arabic Manuscripts using KNN and SVM Classifiers
    Zafar, Aasim
    Iqbal, Arshad
    [J]. PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT (INDIACOM-2020), 2019, : 83 - 87
  • [4] The Automated Arabic Text Categorization Using SVM and KNN
    Hadi, Wa'el Musa
    Eljinini, Mohammad Ali H.
    Alhawari, Samer
    [J]. KNOWLEDGE MANAGEMENT AND INNOVATION: A BUSINESS COMPETITIVE EDGE PERSPECTIVE, VOLS 1-3, 2010, : 757 - +
  • [5] Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naive Bayes Classifiers
    Dixit, Abhishek
    Mani, Ashish
    Bansal, Rohit
    [J]. ENGINEERING JOURNAL-THAILAND, 2020, 24 (05): : 161 - 172
  • [6] Comparison of Data Reduction Techniques Based on the Performance of SVM-type Classifiers
    Georgescu, Ramona
    Berger, Christian R.
    Willett, Peter
    Azam, Mohammad
    Ghoshal, Sudipto
    [J]. 2010 IEEE AEROSPACE CONFERENCE PROCEEDINGS, 2010,
  • [7] Meta-Classification using SVM Classifiers for Text Documents
    Morariu, Daniel I.
    Vintan, Lucian N.
    Tresp, Volker
    [J]. PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 15, 2006, 15 : 222 - +
  • [8] Making scanned Arabic documents machine accessible using an ensemble of SVM classifiers
    Randa Elanwar
    Wenda Qin
    Margrit Betke
    [J]. International Journal on Document Analysis and Recognition (IJDAR), 2018, 21 : 59 - 75
  • [9] Making scanned Arabic documents machine accessible using an ensemble of SVM classifiers
    Elanwar, Randa
    Qin, Wenda
    Betke, Margrit
    [J]. INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2018, 21 (1-2) : 59 - 75
  • [10] Bayesian Performance Comparison of Text Classifiers
    Zhang, Dell
    Wang, Jun
    Yilmaz, Emine
    Wang, Xiaoling
    Zhou, Yuxin
    [J]. SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 2016, : 15 - 24
12345