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Using Genetic Algorithms for Data Mining Optimization in an Educational Web-Based System
Behrouz Minaei-Bidgoli and William F. Punch
Genetic Algorithms Research and Applications Group (GARAGe)
Department of Computer Science & Engineering
Michigan State University
2340 Engineering Building
East Lansing, MI 48824
email{minaeibi,punch}@cse.msu.edu
http://garage.cse.msu.edu
Abstract. This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; feature weighting is works better than just feature selection.
LNCS 2724, p. 2252 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003