EMPIRICAL EVALUATION OF FEATURE SELECTION TECHNIQUE IN EDUCATIONAL DATA MINING

Abstract

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.