Feature selection method | ML algorithm | BF | Proposed feature | |||||||
---|---|---|---|---|---|---|---|---|---|---|
SLF | SLF + DSF | |||||||||
Prec | Rec | F-meas | Prec | Rec | F-meas | Prec | Rec | F-meas | ||
None | Bayes Net | 0.664 | 0.664 | 0.664 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 |
Naïve Bayes | 0.701 | 0.770 | 0.735 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 | |
Logistic | 0.700 | 0.752 | 0.724 | 0.751 | 0.796 | 0.772 | 0.779 | 0.847 | 0.801 | |
MLP | 0.739 | 0.796 | 0.761 | 0.782 | 0.810 | 0.795 | 0.770 | 0.810 | 0.788 | |
J48 | 0.681 | 0.761 | 0.719 | *0.796 | *0.847 | *0.811 | 0.779 | 0.847 | 0.801 | |
Random Forest | 0.689 | 0.814 | 0.747 | 0.740 | 0.847 | 0.790 | 0.740 | 0.847 | 0.790 | |
Random Tree | 0.736 | 0.708 | 0.721 | 0.773 | 0.788 | 0.781 | 0.776 | 0.766 | 0.771 | |
CA | Bayes Net | 0.707 | 0.770 | 0.735 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 |
Naïve Bayes | 0.664 | 0.664 | 0.664 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 | |
Logistic | 0.700 | 0.752 | 0.724 | 0.751 | 0.796 | 0.772 | 0.779 | 0.847 | 0.801 | |
MLP | 0.739 | 0.796 | 0.761 | 0.782 | 0.810 | 0.795 | 0.770 | 0.810 | 0.788 | |
J48 | 0.681 | 0.761 | 0.719 | *0.796 | *0.847 | 0.811 | 0.779 | 0.847 | 0.801 | |
Random Forest | 0.689 | 0.814 | 0.747 | 0.740 | *0.847 | 0.790 | 0.740 | 0.847 | 0.790 | |
Random Tree | 0.736 | 0.708 | 0.721 | 0.773 | 0.788 | 0.781 | 0.776 | 0.766 | 0.771 | |
GR | Bayes Net | 0.707 | 0.770 | 0.735 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 |
Naïve Bayes | 0.664 | 0.664 | 0.664 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 | |
Logistic | 0.700 | 0.752 | 0.724 | 0.751 | 0.796 | 0.772 | 0.779 | 0.847 | 0.801 | |
MLP | 0.739 | 0.796 | 0.761 | 0.782 | 0.810 | 0.795 | 0.770 | 0.81 | 0.788 | |
J48 | 0.681 | 0.761 | 0.719 | *0.796 | *0.847 | *0.811 | 0.779 | 0.847 | 0.801 | |
Random Forest | 0.689 | 0.814 | 0.747 | 0.740 | *0.847 | 0.790 | 0.740 | 0.847 | 0.790 | |
Random Tree | 0.736 | 0.708 | 0.721 | 0.773 | 0.788 | 0.781 | 0.776 | 0.766 | 0.771 | |
IG | Bayes Net | 0.707 | 0.770 | 0.735 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 |
Naïve Bayes | 0.664 | 0.664 | 0.664 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 | |
Logistic | 0.700 | 0.752 | 0.724 | 0.751 | 0.796 | 0.772 | 0.779 | 0.847 | 0.801 | |
MLP | 0.739 | 0.796 | 0.761 | 0.782 | 0.810 | 0.795 | 0.770 | 0.810 | 0.788 | |
J48 | 0.681 | 0.761 | 0.719 | *0.796 | *0.847 | *0.811 | 0.779 | 0.847 | 0.801 | |
Random Forest | 0.689 | 0.814 | 0.747 | 0.740 | *0.847 | 0.790 | 0.740 | 0.847 | 0.790 | |
Random Tree | 0.736 | 0.708 | 0.721 | 0.773 | 0.788 | 0.781 | 0.776 | 0.766 | 0.771 | |
OneR | Bayes Net | 0.664 | 0.664 | 0.664 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 |
Naïve Bayes | 0.728 | 0.717 | 0.722 | 0.764 | 0.759 | 0.762 | 0.786 | 0.818 | 0.800 | |
Logistic | 0.700 | 0.752 | 0.724 | 0.751 | 0.796 | 0.772 | 0.779 | 0.847 | 0.801 | |
MLP | 0.739 | 0.796 | 0.761 | 0.782 | 0.810 | 0.795 | 0.77 | 0.81 | 0.788 | |
J48 | 0.681 | 0.761 | 0.719 | *0.796 | *0.847 | *0.811 | 0.779 | 0.847 | 0.801 | |
Random Forest | 0.689 | 0.814 | 0.747 | 0.740 | *0.847 | 0.790 | 0.74 | 0.847 | 0.790 | |
Random Tree | 0.736 | 0.708 | 0.721 | 0.773 | 0.788 | 0.781 | 0.776 | 0.766 | 0.771 | |
PCA | Bayes Net | 0.681 | 0.761 | 0.719 | 0.770 | 0.810 | 0.788 | 0.806 | *0.854 | 0.816 |
Naïve Bayes | 0.749 | 0.743 | 0.746 | 0.770 | 0.810 | 0.788 | 0.806 | *0.854 | 0.816 | |
Logistic | 0.688 | 0.805 | 0.742 | 0.740 | 0.847 | 0.790 | 0.740 | 0.847 | 0.790 | |
MLP | 0.700 | 0.752 | 0.724 | 0.742 | 0.759 | 0.750 | 0.782 | 0.832 | 0.802 | |
J48 | 0.691 | 0.823 | 0.751 | 0.738 | 0.832 | 0.782 | 0.740 | 0.847 | 0.790 | |
Random Forest | 0.688 | 0.805 | 0.742 | 0.740 | *0.847 | 0.790 | 0.740 | 0.847 | 0.790 | |
Random Tree | 0.741 | 0.752 | 0.747 | 0.766 | 0.766 | 0.766 | *0.825 | 0.839 | *0.831 |