An Evolutionary Algorithm for the Discovery of Rare Class Association Rules in Learning Management Systems
Created by W.Langdon from
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- @Article{2014-AI-Luna,
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author = "J. M. Luna and C. Romero and J. R. Romero and
S. Ventura",
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title = "An Evolutionary Algorithm for the Discovery of Rare
Class Association Rules in Learning Management
Systems",
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journal = "Applied Intelligence",
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year = "2015",
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volume = "42",
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number = "3",
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pages = "501--513",
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month = apr,
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keywords = "genetic algorithms, genetic programming, Rare
association rules, Grammar guided genetic programming,
Evolutionary computation, Educational data mining",
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publisher = "Springer US",
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language = "English",
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ISSN = "0924-669X",
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URL = "http://dx.doi.org/10.1007/s10489-014-0603-4",
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DOI = "doi:10.1007/s10489-014-0603-4",
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size = "13 pages",
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abstract = "Association rule mining, an important data mining
technique, has been widely focused on the extraction of
frequent patterns. Nevertheless, in some application
domains it is interesting to discover patterns that do
not frequently occur, even when they are strongly
related. More specifically, this type of relation can
be very appropriate in e-learning domains due to its
intrinsic imbalanced nature. In these domains, the aim
is to discover a small but interesting and useful set
of rules that could barely be extracted by traditional
algorithms founded in exhaustive search-based
techniques. In this paper, we propose an evolutionary
algorithm for mining rare class association rules when
gathering student usage data from a Moodle system. We
analyse how the use of different parameters of the
algorithm determine the rule characteristics, and
provides some illustrative examples of them to show
their interpretability and usefulness in e-learning
environments. We also compare our approach to other
existing algorithms for mining both rare and frequent
association rules. Finally, an analysis of the rules
mined is presented, which allows information about
students' unusual behaviour regarding the achievement
of bad or good marks to be discovered.",
- }
Genetic Programming entries for
Jose Maria Luna
Cristobal Romero Morales
Jose Raul Romero Salguero
Sebastian Ventura
Citations