Decision Rule Induction in a Learning Content Management System
A learning content management system (LCMS) is an
environment to support web-based learning content development.
Primary function of the system is to manage the learning process as
well as to generate content customized to meet a unique requirement
of each learner. Among the available supporting tools offered by
several vendors, we propose to enhance the LCMS functionality to
individualize the presented content with the induction ability. Our
induction technique is based on rough set theory. The induced rules
are intended to be the supportive knowledge for guiding the content
flow planning. They can also be used as decision rules to help
content developers on managing content delivered to individual
learner.
[1] Y. Caballero, R. Bello, A. Taboada, A. Nowe, M. Garcia, and G. Casas,
"A new measure based in the rough set theory to estimate the training set
quality", Proc.8th Int. Symp. Symbolic and Numeric Algorithms for
Scientific Computing, pp.133-140, 2006.
[2] B. Chapman and B. Hall, Learning Content Management System.
Brandonhall.com, New York, 2005.
[3] Z. Chen, Computational Intelligence for Decision Support, CRC Press,
2000.
[4] K. Cios, W. Pedrycz, and R. Swiniarski, Data Mining Methods for
Knowledge Discovery, Kluwer Academic Publishers, 1998.
[5] C.W. Holsapple and A.B. Whinston, Decision Support Systems: A
Knowledge-Based Approach, West Publishing Company, 1996.
[6] X. Hu, "Using rough sets theory and database operations to construct a
good ensemble of classifiers for data mining application", Proc.IEEE
ICDM, pp.233-240, 2001.
[7] J. Komorowski, L. Polkowski, and A. Skowron, "Rough sets: A
tutorial", In S.K. Pal and A. Skowron (eds.), Rough Fuzzy
Hybridization: A New Trend in Decision-Making, Springer, pp.3-98,
1999.
[8] A. Lenarcik and Z. Piasta, "Probabilistic rough classifiers with mixture
of discrete and continuous variables", In T.Y. Lin and N. Cercone (eds.),
Rough Sets and Data Mining: Analysis for Imprecise Data, Kluwer
Academic Publishers, pp.373-383, 1997.
[9] D. Miao and L. Hou, "A comparison of rough set methods and
representative inductive learning algorithms", Fundamenta Informaticae,
vol.59, pp.203-218, 2004.
[10] P. Pattaraintakorn, N. Cercone, and K. Naruedomkul, "Hybrid intelligent
systems: Selecting attributes for soft computing analysis", Proc.29th Int.
Conf. Computer Software and Applications, pp.319-325, 2006.
[11] S. Pal and P. Mitra, "Case generation using rough sets with fuzzy
representation", IEEE Trans. Knowledge and Data Engineering, vol.16,
no.3, pp.292-300, 2004.
[12] Z. Pawlak, "Rough sets", Int. Journal of Information and Computer
Science, vol.11, no.5, pp.341-356, 1982.
[13] Z. Pawlak, Rough Sets, Theoretical Aspects of Reasoning about Data,
Kluwer Academic Publishers, 1991.
[14] Z. Pawlak, J. Grzymala-Busse, R. Slowinski, and W. Ziarko, "Rough
sets", Communications of the ACM, vol.38, no.11, pp.88-95, 1995.
[15] J. Peters, D. Lockery, and S. Ramanna, "Monte Carlo off-policy
reinforcement learning; A rough set approach", Proc. 5th Int. Conf.
Hybrid Intelligent Systems, pp.187-192, 2005.
[16] F. Radermacher, "Decision support systems: Scope and potential",
Decision Support Systems, vol.12, pp.257-265, 1994.
[17] A. Skowron and C. Rauszer, "The discernibility matrices and functions
in information systems", In R. Slowinski (ed.), Intelligent Decision
Support, Handbook of Applications and advances of the Rough Set
Theory, Kluwer Academic Publishers, pp.331-362, 1992.
[18] R. Swiniarski, "Rough sets and principal component analysis and their
applications in feature extraction and selection, data model building and
classification", In S. Pal and A. Skowron (eds.), Fuzzy Sets, Rough Sets
and Decision Making Processes, Springer, 1998.
[19] L. Yang and L. Yang, "Study of a cluster algorithm based on rough sets
theory", Proc. 6th Int. Conf. Intelligent Systems Design and Applications,
pp.492-496, 2006.
[20] W. Ziarko, "The discovery, analysis, and representation of data
dependencies in databases", In G. Piatetsky-Shapiro and W.J. Frawley
(eds.), Knowledge Discovery in Databases, AAAI Press, pp.195-209,
1991.
[1] Y. Caballero, R. Bello, A. Taboada, A. Nowe, M. Garcia, and G. Casas,
"A new measure based in the rough set theory to estimate the training set
quality", Proc.8th Int. Symp. Symbolic and Numeric Algorithms for
Scientific Computing, pp.133-140, 2006.
[2] B. Chapman and B. Hall, Learning Content Management System.
Brandonhall.com, New York, 2005.
[3] Z. Chen, Computational Intelligence for Decision Support, CRC Press,
2000.
[4] K. Cios, W. Pedrycz, and R. Swiniarski, Data Mining Methods for
Knowledge Discovery, Kluwer Academic Publishers, 1998.
[5] C.W. Holsapple and A.B. Whinston, Decision Support Systems: A
Knowledge-Based Approach, West Publishing Company, 1996.
[6] X. Hu, "Using rough sets theory and database operations to construct a
good ensemble of classifiers for data mining application", Proc.IEEE
ICDM, pp.233-240, 2001.
[7] J. Komorowski, L. Polkowski, and A. Skowron, "Rough sets: A
tutorial", In S.K. Pal and A. Skowron (eds.), Rough Fuzzy
Hybridization: A New Trend in Decision-Making, Springer, pp.3-98,
1999.
[8] A. Lenarcik and Z. Piasta, "Probabilistic rough classifiers with mixture
of discrete and continuous variables", In T.Y. Lin and N. Cercone (eds.),
Rough Sets and Data Mining: Analysis for Imprecise Data, Kluwer
Academic Publishers, pp.373-383, 1997.
[9] D. Miao and L. Hou, "A comparison of rough set methods and
representative inductive learning algorithms", Fundamenta Informaticae,
vol.59, pp.203-218, 2004.
[10] P. Pattaraintakorn, N. Cercone, and K. Naruedomkul, "Hybrid intelligent
systems: Selecting attributes for soft computing analysis", Proc.29th Int.
Conf. Computer Software and Applications, pp.319-325, 2006.
[11] S. Pal and P. Mitra, "Case generation using rough sets with fuzzy
representation", IEEE Trans. Knowledge and Data Engineering, vol.16,
no.3, pp.292-300, 2004.
[12] Z. Pawlak, "Rough sets", Int. Journal of Information and Computer
Science, vol.11, no.5, pp.341-356, 1982.
[13] Z. Pawlak, Rough Sets, Theoretical Aspects of Reasoning about Data,
Kluwer Academic Publishers, 1991.
[14] Z. Pawlak, J. Grzymala-Busse, R. Slowinski, and W. Ziarko, "Rough
sets", Communications of the ACM, vol.38, no.11, pp.88-95, 1995.
[15] J. Peters, D. Lockery, and S. Ramanna, "Monte Carlo off-policy
reinforcement learning; A rough set approach", Proc. 5th Int. Conf.
Hybrid Intelligent Systems, pp.187-192, 2005.
[16] F. Radermacher, "Decision support systems: Scope and potential",
Decision Support Systems, vol.12, pp.257-265, 1994.
[17] A. Skowron and C. Rauszer, "The discernibility matrices and functions
in information systems", In R. Slowinski (ed.), Intelligent Decision
Support, Handbook of Applications and advances of the Rough Set
Theory, Kluwer Academic Publishers, pp.331-362, 1992.
[18] R. Swiniarski, "Rough sets and principal component analysis and their
applications in feature extraction and selection, data model building and
classification", In S. Pal and A. Skowron (eds.), Fuzzy Sets, Rough Sets
and Decision Making Processes, Springer, 1998.
[19] L. Yang and L. Yang, "Study of a cluster algorithm based on rough sets
theory", Proc. 6th Int. Conf. Intelligent Systems Design and Applications,
pp.492-496, 2006.
[20] W. Ziarko, "The discovery, analysis, and representation of data
dependencies in databases", In G. Piatetsky-Shapiro and W.J. Frawley
(eds.), Knowledge Discovery in Databases, AAAI Press, pp.195-209,
1991.
@article{"International Journal of Information, Control and Computer Sciences:63671", author = "Nittaya Kerdprasop and Narin Muenrat and Kittisak Kerdprasop", title = "Decision Rule Induction in a Learning Content Management System", abstract = "A learning content management system (LCMS) is an
environment to support web-based learning content development.
Primary function of the system is to manage the learning process as
well as to generate content customized to meet a unique requirement
of each learner. Among the available supporting tools offered by
several vendors, we propose to enhance the LCMS functionality to
individualize the presented content with the induction ability. Our
induction technique is based on rough set theory. The induced rules
are intended to be the supportive knowledge for guiding the content
flow planning. They can also be used as decision rules to help
content developers on managing content delivered to individual
learner.", keywords = "Decision rules, Knowledge induction, Learning
content management system, Rough set.", volume = "2", number = "3", pages = "935-5", }
