Kannan, Balakrishnan; Julie, David M(MECS, November , 2013)
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Abstract:
Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.
Description:
I.J. Intelligent Systems and Applications, 2013, 12, 34-52
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
This paper highlights the prediction of Learning
Disabilities (LD) in school-age children using two classification
methods, Support Vector Machine (SVM) and Decision Tree (DT),
with an emphasis on applications of data mining. About 10% of
children enrolled in school have a learning disability. Learning
disability prediction in school age children is a very complicated
task because it tends to be identified in elementary school where
there is no one sign to be identified. By using any of the two
classification methods, SVM and DT, we can easily and accurately
predict LD in any child. Also, we can determine the merits and
demerits of these two classifiers and the best one can be selected for
the use in the relevant field. In this study, Sequential Minimal
Optimization (SMO) algorithm is used in performing SVM and J48
algorithm is used in constructing decision trees.
Description:
(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (2) , 2011, 829-835