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Application of Decision Tree Algorithm to the Identification of Students with Learning Disabilities
Decision tree;Learning disabilities;Feature selection;Machine learning
|Issue Date: ||2013-04-22T07:38:20Z
The characteristics of school-age children with learning disabilities (LD) are not as obvious as students with other physical or psychological disabilities. As a result, the diagnose of students with LD has long been a very difficult issue that requires a lot of time and extensive manpower. Accordingly, there may be many potential LD students un-identified and unable to receive appropriate special education services. This objective of this study is to apply data mining technique to the raw data that are used in manual LD diagnosis process, and try to find
explicit rules that could assist LD diagnosis personnel in the future. The machine learning algorithm we adopted is decision tree algorithm. In addition, various feature selection pre-processing algorithms are experimented on the data prior to the application of decision tree technique. Our experimental results show that, through some proper pre-processing procedure, the rules that are generated by decision tree technique do considerably improve the classification accuracy and thus may be helpful in practical diagnosis application.
|Relation: ||Journal of Information Technology and Applications, 2(2): 107-115|
|Appears in Collections:||[資訊管理學系所] 期刊論文|
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