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2004
Springer

Optimizing Classification Ensembles via a Genetic Algorithm for a Web-Based Educational System

9 years 17 days ago
Optimizing Classification Ensembles via a Genetic Algorithm for a Web-Based Educational System
Classification fusion combines multiple classifications of data into a single classification solution of greater accuracy. Feature extraction aims to reduce the computational cost of feature measurement, increase classifier efficiency, and allow greater classification accuracy based on the process of deriving new features from the original features. This paper represents an approach for classifying students in order to predict their final grades based on features extracted from logged data in an educational web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. By weighing feature vectors representing feature importance using a Genetic Algorithm (GA) we can optimize the prediction accuracy and obtain a marked improvement over raw classification. We further show that when the number of features is few, feature weighting and transformation into a new space works efficiently compared to the feature subset selection. This a...
Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where SSPR
Authors Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F. Punch
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