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ARPN Journal of Science and Technology >> Volume 7, Issue 1, January 2017

ARPN Journal of Science and Technology

Empirical Evaluation of Feature Selection Technique in Educational Data Mining

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Author A.S. Kavitha, R. Kavitha, J. Viji Gripsy
ISSN 2225-7217
On Pages 1103-1112
Volume No. 2
Issue No. 11
Issue Date January 01, 2013
Publishing Date January 01, 2013
Keywords Feature Selection, One R, PART, K-means algorithm, RELIEF algorithm


In machine learning the classification task is commonly referred to as supervised learning. In supervised learning there is a specified set of classes and objects are labeled with the appropriate class. The goal is to generalize from the training objects that will enable novel objects to be identified as belonging to one of the classes. Evaluating the performance of learning algorithms is a fundamental aspect of machine learning. The primary objective of this thesis is to study the classification accuracy using feature selection with machine learning algorithms. Feature selection is considered successful if the dimensionality of the data is reduced and accuracy of a learning algorithm improves or remains the same. Hence our contribution in this research is to prepare an educational dataset with real time feedback from students and try to apply the same with weka tool to measure the classification accuracy. Some part of implementation is compiled with weka, which is written in java and experiment with weka explorer.

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