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Reseach Article

Article:Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children

by Julie M. David, Kannan Balakrishnan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 11
Year of Publication: 2010
Authors: Julie M. David, Kannan Balakrishnan
10.5120/1432-1931

Julie M. David, Kannan Balakrishnan . Article:Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children. International Journal of Computer Applications. 9, 11 ( November 2010), 7-14. DOI=10.5120/1432-1931

@article{ 10.5120/1432-1931,
author = { Julie M. David, Kannan Balakrishnan },
title = { Article:Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 9 },
number = { 11 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 7-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume9/number11/1432-1931/ },
doi = { 10.5120/1432-1931 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:18.111425+05:30
%A Julie M. David
%A Kannan Balakrishnan
%T Article:Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children
%J International Journal of Computer Applications
%@ 0975-8887
%V 9
%N 11
%P 7-14
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper highlights the two machine learning approaches, viz. Rough Sets and Decision Trees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications of data mining. Learning disability prediction is a very complicated task. By using these two approaches, we can easily and accurately predict LD in any child and also we can determine the best classification method. In this study, in rough sets the attribute reduction and classification are performed using Johnson’s reduction algorithm and Naive Bayes algorithm respectively for rule mining and in construction of decision trees, J48 algorithm is used. From this study, it is concluded that, the performance of decision trees are considerably poorer in several important aspects compared to rough sets. It is found that, for selection of attributes, rough sets is very useful especially in the case of inconsistent data and it also gives the information about the attribute correlation which is very important in the case of learning disability.

References
  1. Abraham, Ajith; Falcón, Rafael; Bello, Rafael (Eds.).2009. Rough Set Theory: A True Landmark in Data Analysis, Series: Studies in Computational Intelligence, Vol. 174, ISBN: 978-3-540-89920-4,
  2. Ashwin Kothari and Avinash Keskar, 2009. Paper on Rough Set Approach for Overall Performance Improvement of an Unsupervised ANN Based Pattern Classifier, Journal on Advanced Computational Intelligence and Intelligent Information, Vol. 13, No.4, 434-440
  3. Blackwell Synergy. 2006 Learning Disabilities Research Practices, Volume 22
  4. Chen R.S, Wu R.C., Chang C.C 2005. Using data mining technology to design an intelligent CIM system for IC manufacturing. In: proceedings of sixth international conference on software engineering, artificial intelligence, network, parallel distribution, computation self assembly wireless network, SNPD/SAWN, Towson, MD, USA, 70-75
  5. Crealock Carol, Kronick Doreen. 1993. Children and Young People with Specific Learning Disabilities, Guides for Special Education, No. 9, UNESCO
  6. Fayyad U.M., 1996. From Data Mining to Knowledge Discovery:An Overview- Advances in Knowledge Discovery and Data Mining:-34, AAAI Press/MIT Press, ISBN 0-262-56097-6
  7. Frawley and Piaatetsky.1996. Shaping Knowledge Discovery in Database; An Overview, The AAAI/MIT press, Menlo Park
  8. Greco S., Matarazzo B, Slowinski R. 2000. Dealing with missing data in rough set analysis of multi-attribute and multi-criteria decision problem, Kluwer Academic Publishers, Boston Dordrecht, London
  9. Grzymala-Busse JW,1988. Knowledge Acquisition under Uncertainty-A Rough Set Approach. Journal of Intelligent & Robotic Systems, Vol 1, 3-16
  10. Hameed Al-Qaheri, Aboul Ella Hassanien and Ajith Abraham. 2008. Discovering Stock Price Prediction Rules using Rough Sets
  11. Han Jiawei and Kamber Micheline, 2008. Data Mining-Concepts and Techniques, Second Edition, Morgan Kaufmann - Elsevier Publishers, ISBN : 978-1-55860-901-3
  12. Hsinchun Chen, Sherrilynne S. Fuller, Carol Friedman and William Hersh, 2005. Knowledge Discovery in Data Mining and Text Mining in Medical Informatics, Chapter 1, 3-34
  13. Iftikar U. Sikder, Toshinori Munakata, 2009. Application of rough set and decision tree for characterization of premonitory factors of low seismic activity, Expert system with applications, Elsevier, Vol 36, 102-110, available at www.sciencedirect.com
  14. Julie M. David, Pramod K.V, 2008. Paper on Prediction of Learning Disabilities in School Age Children using Data Mining Techniques. In: Proceedings of AICTE Sponsored National Conference on Recent Developments and Applications of Probability Theory, Random Process and Random Variables in Computer Science, T. Thrivikram, P. Nagabhushan, M.S. Samuel (eds), 139-146
  15. Julie M. David, Kannan Balakrishnan. 2009. Paper on Prediction of Frequent Signs of Learning Disabilities in School Age Children using Association Rules. In: Proceedings of the International Conference on Advanced Computing, ICAC 09, MacMillion Publishers India Ltd, NYC, ISBN 10:0230-63915-1, ISBN 13:978-0230-63915-7, 202-207
  16. Julie M. David, Kannan Balakrishnan, 2010. Paper on Prediction of Learning Disabilities in School Age Children using Decision Tree. In: Proceedings of the International Conference on Recent Trends in Network Communications- CCIS Vol. 90, Part 3, N. Meghanathan, Selma Boumerdassi, Nabendu Chaki, Dhinaharan Nagamalai (eds), Springer- Verlag Berlin Heidelberg, ISSN:1865-0929(print) 1865-0937 (online), ISBN 978-3-642-14492-9 (print) 978-3-642-14493-6 (online), DOI : 10.1007/978-3-642-14493-6_55, 5
  17. Julie M. David, Kannan Balakrishnan, Oct.2010. Significance of Classification Techniques in Prediction of Learning Disabilities in School Age Children, International Journal of Artificial Intelligence & Applications (IJAIA), Vol 1, No. 4, DOI:10.5121/ijaia.2010.1409, 111-120
  18. Kusiak A, Kurasek C. March 2006. Data mining of printed circuit board defects, IEEE Trans, Rob Autom, Vol 17, No.2, 74-384
  19. Marcin Perzyk, Artur Soroczynski, 2010. Knowledge extraction tools for design and control of industrial process.
  20. Matteo Magnani. 2003. Technical report on Rough Set Theory for Knowledge Discovery in Data Bases
  21. Pawlak Z. 1982. Rough Sets, International Journal on Computers and Information Science, Vol. 11, 341-356
  22. Quinlan J.R., 1986. Induction on decision trees, Machine learning, 1(1):81-106
  23. Rod Paige, (Secretary). 2002. US Department of Education, Twenty-fourth Annual Report to Congress on the Implementation of the Individuals with disabilities Education Act-To Assure the Free Appropriate Public Education of all Children with Disabilities
  24. Sally Jo Cunningham and Geoffrey Holmes, 1999. Developing innovative applications in agricultural using data mining. In: Proceedings of the Southeast Asia Regional Computer Confederation Conference
  25. Stuart R., Peter N., 2009. Artificial Intelligence – A Modern approach, Pearson Prentice Hall
  26. Tan Pang-Ning, Steinbach Michael, Kumar Vipin, 2008. Introduction to Data Mining, Low Price Edition, Pearson Education, Inc., ISBN 978-81-317-1472-0
  27. Tseng T.L., Jothishankar M.C., Wu T., Xing G., Jiang F. 2010, Applying data mining approaches for defect diagnosis in manufacturing, World Academy of Science, Engineering and Technology, 61
  28. Wang K., Aug. 2007. Applying data mining to manufacturing; The nature and implications, Journal of Intelligent Manufacturing, Vol. 18, No.4, 487-495
  29. Witten I.H, Frank Ibe. 2005. Data Mining Practical Machine Learning Tools and Techniques, Morgan Kaufmann Elsevier Publishers, 2nd Edition, ISBN : 13: 978-81-312-0050-6
Index Terms

Computer Science
Information Sciences

Keywords

Decision Tree Learning Disability Rough Sets Rule Mining Support Confidence