Integrating Noun-Based Feature Ranking and Selection Methods with Arabic Text Associative Classification Approach

Arabian Journal for Science and Engineering

Volumn 39, Issue 11, 2014, Pages 7807-7822

  Ghareb, Abdullah S ^a   Hamdan, Abdul Razak ^a   Bakar, Azuraliza Abu ^a  

^a UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

Author keywords

Arabic text; Associative classification; Category association rule; Feature ranking; Feature selection; Noun extraction

Indexed keywords

EID: 84909989533     PISSN: 2193567X     EISSN: 21914281     Source Type: Journal    
DOI: 10.1007/s13369-014-1304-3     Document Type: Article

References (38)

1. Makrehchi, M.: Feature Ranking for Text Classifiers. Doctoral Dissertation. Electrical and Computer Engineering Department, University of Waterloo, Ontario, Canada (2007)
2. Al-Shamsi, F.; Guessoum, A.: A hidden markov model-based POS tagger for Arabic. In: JADT06, 8 Journées Internationales d’Analyse Statistique des Données Textuelles, pp. 31–42. France (2006)
3. Al-Sughaiyer I., Al-Kharashi I.: Arabic morphological analysis techniques: a comprehensive survey. J. Am. Soc. Info. Sci. Technol. 55(3), 189–213 (2004)
4. Al-Shalabi, R.; Kanaan, G.; Al-Sarayreh, B.; Khanfar, K.; Al-Ghonmein, A.; Talhouni, H.; Al-Azazmeh, S.: Proper noun extracting algorithm for Arabic language. In: International Conference of Information Technology, Special issue of the International Journal of the Computer, the Internet and Management, 17. Thailand (2009)
5. Bouras, C.; Tsogkas, V.: Noun retrieval effect on text summarization and delivery of personalized news articles to the user’s desktop. Data Knowl. Eng. J. (2010). doi:10.1016/j.datak.2010.02.005
6. Chen J., Huang H., Tian S., Qua Y.: Feature selection for text classification with Naïve Bayes. Expert Syst. Appl. 36, 5432–5435 (2009)
7. Yang, Y.; Pedersen, J.O.: A Comparative study on feature selection in text categorization. In: Proceedings of the 14th International Conference on Machine Learning, pp. 412–420. Nashville, USA (1997)
8. Mesleh, A.: Support vector machines based Arabic language text classification system: feature selection comparative study. In: Tarek, S. (ed.) Advances in Computer and Information Sciences and Engineering, pp. 11–16. Springer, Netherlands (2008)
9. Chiang D.-A., Keh H.-C., Huang H.-H., Chyr D.: The Chinese text categorization system with association rule and category priority. Expert Syst. Appl. 35, 102–110 (2008)
10. Abdul-Rahman S., Abu Bakar A., Hussein Z.: An intelligent data pre-processing for complex dataset. Intell. Data Anal. 16(2), 305–325 (2012)
11. Thabtah F.: A Review of associative classification mining. Knowl. Eng. Rev. 22(1), 37–65 (2007)
12. Liu, B.; Hsu, W.; Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the International Conference on Knowledge Discovery Data Mining, pp. 80–86. New York, NY: AAAI Press (1998)
13. Xu, X.; Han, G.; Min, H.: A novel algorithm for associative classification of images blocks. In: Proceedings of the 4th IEEE International Conference on Computer and Information Technology, pp. 46–51. Lian, Shiguo, China (2004)
14. Yin, X.; Han, J.: CPAR: classification based on predictive association rule. In: Proceedings of the SIAM International Conference on Data Mining, pp. 369–376. SIAM Press, San Francisco, CA (2003)
15. Antonie, M.; Zaiane, O.: Text document categorization by term association. In: IEEE International Conference on Data Mining (ICDM’02), pp. 19–26. Maebashi, Japan (2002)
16. Christopher J.: A statistical approach for associative classification. Eur. J. Sci. Res. 58(2), 140–147 (2011)
17. Ibrahim S., Chandran K., Christopher J.: An evolutionary approach for rule set selection in a class based associative classifier. Eur. J. Sci. Res. 50(3), 417–425 (2011)
18. Thabtah, F.; Hadi, W.; Abu-Mansour, H.; McCluskey, L.: A New rule pruning text categorization method. In: 7th International Multi-Conference on Systems, Signals and Devices, pp. 1–6. IEEE, London, UK (2010)
19. Bakar, A.A.; Othman, Z.; Nizam, S.; Ismail, R.: Development of knowledge model for insurance product decision using the associative classification approach. In: The International Conference on Intelligent Systems Design and Applications, pp. 1481–1486. Cairo, Egypt, IEEE (2010)
20. Soni S., Vyas O.P.: Using associative classifiers for predictive analysis in health care data mining. Int. J. Comput. Appl. 4(5), 33–34 (2010)
21. Soni J., Ansari U., Sharma D.: Intelligent and effective heart disease prediction system using weighted associative classifiers. Int. J. Comput. Sci. Eng. 3(6), 2385–2392 (2011)
22. Al-Saleem S.: Associative classification to categorize Arabic data sets. Int. J. ACM Jordan 1(3), 118–127 (2010)
23. Al-Radaideh Q., Al_Shawakfeh E., Ghareb A.S., Abu Salem H.: An approach for Arabic text categorization using association rule mining. Int. J. Comput. Process. Orient. Lang. 23(1), 81–106 (2011)
24. Hattab A., Hussien A.: Arabic content classification system using statistical Bayes classifier with words detection and correction. World Comput. Sci. Inf. Technol. J. 2(6), 193–196 (2012)
25. Al-diabat M.: Arabic text categorization using classification rule mining. Appl. Math. Sci. 6(81), 4033–4046 (2012)
26. Wahbeh A., Al-Kabi M., Al-Radaideh Q., Al-Shawakfa E., AlSmadi I.: The effect of stemming on Arabic text categorization: An empirical study. Int. J. Inf. Retr. Res. 1(3), 54–70 (2011)
27. Khreisat L.: A Machine learning approach for Arabic text classification using n-gram frequency statistics. J. Informetr. 3, 72–77 (2009)
28. Zahran, M.; Kanaan, G.: Text feature selection using particle swarm optimization algorithm. World Appl. Sci. J. 7 (Special Issue of Computer & IT), 69–74 (2009)
29. Harrag F., Al-Qawasmah E.: Improving Arabic text categorization using neural network with SVD. J. Digit. Inf. Manag. 8(4), 233–239 (2010)
30. Jbara K.: Knowledge discovery in Al-Hadith using text classification algorithm. J. Am. Sci. 6(11), 409–419 (2010)
31. Al-Serhan, H.; Al Shalabi, R.; Kannan, G.: New approach for extracting Arabic roots. In: Proceedings of the 2003 Arab Conference on Information Technology, pp. 42–59. Potland, Oregon, USA (2003)
32. Lee, D.G.; Rim, H.C.; Lim, H.S.: A syllable based word recognition model for Korean noun extraction. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, ACL, pp. 471–478. Sapporo, Japan (2003)
33. Alkhatib, K.; Badarneh, A.: Automatic extraction of Arabic multi-words terms. In: Proceedings of the International Multi-Conference on Computer Science and Information Technology, pp. 411–418 (2010)
34. Abdel Rahman S., Elarnaoty M., Magdy M., Fahmy A.: Integrated machine learning techniques for Arabic named entity recognition. Int. J. Comput. Sci. Issues 7(4), 27–36 (2010)
35. Abdul-Hamid, A.; Darwish, K.: Simplified feature set for Arabic named entity recognition. In: Proceedings of the 2010 Named Entities Workshop, ACL, 16 July 2010, pp. 110–115. Uppsala, Sweden (2010)
36. Salton G., Buckley C.: Term weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
37. Mutter, S.: Classification using Association Rules. Master thesis, Department of Computer Science, University of Freiburg, Germany (2004)
38. Abbas M., Smaili K., Berkani D.: Evaluation of topic identification methods on Arabic corpora. J. Digit. Inf. Manag. 9(5), 185–192 (2011)