期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：2
页码：829-835
出版社：TechScience Publications
摘要：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.
关键词：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.
