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.
