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
Kannan, Balakrishnan; Rafidha Rahiman, K A; Sherly, K B(IEEE, 2011)
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Abstract:
In our study we use a kernel based classification
technique, Support Vector Machine Regression for predicting the
Melting Point of Drug – like compounds in terms of Topological
Descriptors, Topological Charge Indices, Connectivity Indices
and 2D Auto Correlations. The Machine Learning model was
designed, trained and tested using a dataset of 100 compounds
and it was found that an SVMReg model with RBF Kernel could
predict the Melting Point with a mean absolute error 15.5854 and
Root Mean Squared Error 19.7576