Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/6305
Title: A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier
Authors: Noormadinah Allias
Megat NorulAzmi Megat Mohamed Noor
Mohd. Nazri Ismail
Kim de Silva
(UniKL MIIT)
Keywords: swarm size
taguchi method
orthogonal array
learning algorithms
Issue Date: 24-Apr-2014
Series/Report no.: Proceeding of: International Conference on Artificial Intelligence, Modelling & Simulation;
Abstract: A performance of anti-spam filter not only depends on the number of features and types of classifier that are used, but it also depends on the other parameter settings. Deriving from previous experiments, we extended our work by investigating the effect of population sizes from our proposed method of feature selection on different learning classifier algorithms using Random Forest, Voting, Decision Tree, Support Vector Machine and Stacking. The experiment was conducted on Ling-Spam email dataset. The results showed that the Decision Tree with the smallest size of population is able to give the best result compared to NB, SVM, RF, stacking and voting.A performance of anti-spam filter not only depends on the number of features and types of classifier that are used, but it also depends on the other parameter settings. Deriving from previous experiments, we extended our work by investigating the effect of population sizes from our proposed method of feature selection on different learning classifier algorithms using Random Forest, Voting, Decision Tree, Support Vector Machine and Stacking. The experiment was conducted on Ling-Spam email dataset. The results showed that the Decision Tree with the smallest size of population is able to give the best result compared to NB, SVM, RF, stacking and voting.
URI: http://localhost/xmlui/handle/123456789/6305
Appears in Collections:Conference Papers



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