Automated Categorization Scheme
For Digital Libraries In Distance Learning: A Pattern Recognition Approach

Year 2008, Volume: 9 Issue: 4, 30 - 38, 01.12.2008

Serkan Gunal

Abstract

Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is therefore very important for easy and fast access to the desired information. Improper categorization of data may mislead the students searching the library. Since manual categorization of huge amount of data might be challenging, an automatic and reliable method is needed. In this sense, this paper proposes an automated categorization scheme for digital libraries in distance learning. The categorization scheme is designed and developed by a pattern recognition approach. Effectiveness of the proposed scheme is evaluated on widely used Reuters database. The results of the experimental study verify that the proposed scheme is a good candidate for categorization of digital libraries in distance learning programs.

Keywords

Pattern Recognition, Text Categorization, Digital Library, Distance Learning.

References

• Anadolu University, Yunus Emre Learning Portal. (2008). Website: http://yunusemre.anadolu.edu.tr
• Duda, R. O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, John Wiley & Sons Inc., USA.
• Gunal, S., Ergin, S., Gulmezoglu, M. B., Gerek, O. N. (2006). “On feature extraction for spam e-mail detection”, Lecture Notes in Computer Science, vol.4105, pp.635–642.
• Hettich, S. and Bay, S. D. (1999). The UCI KDD Archive [http://kdd.ics.uci.edu]. Irvine,
• CA: University of California, Department of Information and Computer Science. Jain, A., Zongker, D. (1997). “Feature selection: evaluation, application, and small sample performance”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.19, no.2, pp.153–158.
• Kuncheva, L. I. (2004). Combining Pattern Classifiers, John Wiley & Sons Inc., New Jersey.
• Lau, L. K. (2000). Distance Learning Technologies: Issues, Trends and Opportunities, Idea Group Pub.
• MIT (Massachusetts Institute of Technology), OpenCourseWare system. (2008). Website: http://ocw.mit.edu
• Sebastiani, F. (2002). “Machine learning in automated text categorization”, ACM
• Computing Surveys, vol.34, no.1, March 2002, pp.1–47.
• Selamat, A. and Omatu, S. (2004). “Web page feature selection and classification using neural networks”, Information Sciences, vol.158, pp.69–88.
• Schölkopf, B., Smola, A.J. (2001) Learning with Kernels : Support Vector Machines,
• Regularization, Optimization, and Beyond, MIT Press. Shang, W., Huang, H., Zhu, H., Lin, Y., Qu, Y. and Wang, Z. (2007). “A novel feature selection algorithm for text categorization”, Expert Systems with Applications, vol.33, pp.1–5.
• Theodoridis, S. and Koutroumbas, K. (2003) Pattern Recognition, Academic Press, USA.
• Webb, A. (2002). Statistical Pattern Recognition, John Wiley & Sons Ltd., England.