DocumentCode :
2135664
Title :
Study to Genetic Algorithms for Data Mining Optimization
Author :
Hu Guohua ; Shi Yuemei
Author_Institution :
Dept. of Comput., Xinzhou Teachers Univ., Xinzhou, China
fYear :
2009
fDate :
20-22 Sept. 2009
Firstpage :
1
Lastpage :
4
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.lt further shows that when the number of features is few;feature weighting is works better than just feature selection.
Keywords :
computer aided instruction; data mining; genetic algorithms; pattern classification; data mining optimization; education Web-based system; features extraction; genetic algorithm; Accuracy; Computer science education; Data mining; Design optimization; Educational institutions; Error analysis; Feature extraction; Genetic algorithms; Home computing; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Management and Service Science, 2009. MASS '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4638-4
Electronic_ISBN :
978-1-4244-4639-1
Type :
conf
DOI :
10.1109/ICMSS.2009.5303359
Filename :
5303359
Link To Document :
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