Kannan, Balakrishnan; Julie, David M(IACSIT, February , 2011)
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Abstract:
This paper highlights the prediction of learning
disabilities (LD) in school-age children using rough set theory
(RST) with an emphasis on application of data mining. In
rough sets, data analysis start from a data table called an
information system, which contains data about objects of
interest, characterized in terms of attributes. These attributes
consist of the properties of learning disabilities. By finding the
relationship between these attributes, the redundant attributes
can be eliminated and core attributes determined. Also, rule
mining is performed in rough sets using the algorithm LEM1.
The prediction of LD is accurately done by using Rosetta, the
rough set tool kit for analysis of data. The result obtained from
this study is compared with the output of a similar study
conducted by us using Support Vector Machine (SVM) with
Sequential Minimal Optimisation (SMO) algorithm. It is found
that, using the concepts of reduct and global covering, we can
easily predict the learning disabilities in children
Description:
International Journal of Computer and Electrical Engineering, Vol.3, No.1, February, 2011
1793-8163