Research Article
A Novel Robust Fuzzy Rough Set Model for Feature Selection
Table 2
Comparison of classification performance of different FRS models on KNN in original and noisy data.
| Datasets | Noise level (%) | FRS | β-PFRS | K-trimmed FRS | K-means FRS | K-median FRS | SFRS | RS-FRS |
| Glass | 0 | 0.678 ± 0.134 | 0.650 ± 0.068 | 0.631 ± 0.061 | 0.650 ± 0.078 | 0.655 ± 0.099 | 0.678 ± 0.134 | 0.668 ± 0.031 | 5 | 0.629 ± 0.061 | 0.649 ± 0.027 | 0.630 ± 0.037 | 0.647 ± 0.091 | 0.648 ± 0.043 | 0.655 ± 0.096 | 0.656 ± 0.029 | 10 | 0.628 ± 0.098 | 0.647 ± 0.066 | 0.626 ± 0.110 | 0.645 ± 0.019 | 0.644 ± 0.068 | 0.648 ± 0.089 | 0.649 ± 0.015 |
| Wine | 0 | 0.931 ± 0.072 | 0.926 ± 0.054 | 0.926 ± 0.054 | 0.931 ± 0.072 | 0.931 ± 0.072 | 0.931 ± 0.072 | 0.931 ± 0.072 | 5 | 0.892 ± 0.069 | 0.910 ± 0.045 | 0.925 ± 0.059 | 0.930 ± 0.059 | 0.924 ± 0.062 | 0.920 ± 0.073 | 0.928 ± 0.086 | 10 | 0.876 ± 0.058 | 0.909 ± 0.038 | 0.924 ± 0.035 | 0.912 ± 0.072 | 0.908 ± 0.054 | 0.919 ± 0.071 | 0.926 ± 0.044 |
| Heart | 0 | 0.744 ± 0.069 | 0.759 ± 0.082 | 0.741 ± 0.105 | 0.730 ± 0.078 | 0.730 ± 0.078 | 0.744 ± 0.069 | 0.744 ± 0.069 | 5 | 0.735 ± 0.071 | 0.739 ± 0.059 | 0.740 ± 0.110 | 0.728 ± 0.080 | 0.727 ± 0.013 | 0.741 ± 0.067 | 0.742 ± 0.038 | 10 | 0.719 ± 0.082 | 0.733 ± 0.086 | 0.734 ± 0.044 | 0.726 ± 0.019 | 0.727 ± 0.078 | 0.736 ± 0.054 | 0.736 ± 0.025 |
| Segment | 0 | 0.946 ± 0.109 | 0.949 ± 0.109 | 0.953 ± 0.109 | 0.955 ± 0.109 | 0.953 ± 0.109 | 0.946 ± 0.033 | 0.946 ± 0.109 | 5 | 0.944 ± 0.038 | 0.948 ± 0.036 | 0.949 ± 0.032 | 0.951 ± 0.092 | 0.952 ± 0.034 | 0.945 ± 0.055 | 0.945 ± 0.010 | 10 | 0.941 ± 0.040 | 0.947 ± 0.032 | 0.946 ± 0.053 | 0.949 ± 0.039 | 0.947 ± 0.037 | 0.944 ± 0.071 | 0.944 ± 0.104 |
| Hepatitis | 0 | 0.765 ± 0.107 | 0.765 ± 0.107 | 0.765 ± 0.107 | 0.765 ± 0.107 | 0.765 ± 0.107 | 0.765 ± 0.107 | 0.765 ± 0.107 | 5 | 0.763 ± 0.092 | 0.764 ± 0.108 | 0.763 ± 0.045 | 0.764 ± 0.099 | 0.760 ± 0.123 | 0.764 ± 0.092 | 0.764 ± 0.062 | 10 | 0.750 ± 0.118 | 0.759 ± 0.075 | 0.760 ± 0.075 | 0.764 ± 0.081 | 0.753 ± 0.077 | 0.750 ± 0.028 | 0.762 ± 0.104 |
| ICU | 0 | 0.868 ± 0.162 | 0.879 ± 0.183 | 0.879 ± 0.183 | 0.868 ± 0.129 | 0.868 ± 0.129 | 0.868 ± 0.162 | 0.880 ± 0.042 | 5 | 0.856 ± 0.176 | 0.859 ± 0.087 | 0.860 ± 0.158 | 0.857 ± 0.162 | 0.855 ± 0.146 | 0.861 ± 0.054 | 0.863 ± 0.107 | 10 | 0.850 ± 0.023 | 0.855 ± 0.126 | 0.853 ± 0.101 | 0.856 ± 0.023 | 0.852 ± 0.176 | 0.856 ± 0.010 | 0.862 ± 0.093 |
| German | 0 | 0.716 ± 0.037 | 0.712 ± 0.061 | 0.713 ± 0.045 | 0.713 ± 0.032 | 0.713 ± 0.032 | 0.716 ± 0.037 | 0.716 ± 0.037 | 5 | 0.708 ± 0.038 | 0.710 ± 0.046 | 0.710 ± 0.059 | 0.709 ± 0.037 | 0.712 ± 0.041 | 0.713 ± 0.063 | 0.714 ± 0.031 | 10 | 0.706 ± 0.033 | 0.710 ± 0.035 | 0.708 ± 0.042 | 0.708 ± 0.091 | 0.709 ± 0.032 | 0.710 ± 0.025 | 0.713 ± 0.088 |
| Soy | 0 | 0.850 ± 0.141 | 0.825 ± 0.134 | 0.850 ± 0.141 | 0.850 ± 0.141 | 0.850 ± 0.141 | 0.850 ± 0.141 | 0.850 ± 0.141 | 5 | 0.790 ± 0.062 | 0.819 ± 0.077 | 0.818 ± 0.025 | 0.818 ± 0.018 | 0.816 ± 0.050 | 0.820 ± 0.041 | 0.820 ± 0.112 | 10 | 0.760 ± 0.046 | 0.818 ± 0.082 | 0.810 ± 0.082 | 0.806 ± 0.087 | 0.809 ± 0.246 | 0.816 ± 0.092 | 0.818 ± 0.070 |
| Horse | 0 | 0.864 ± 0.056 | 0.845 ± 0.051 | 0.834 ± 0.060 | 0.834 ± 0.060 | 0.840 ± 0.061 | 0.845 ± 0.051 | 0.864 ± 0.056 | 5 | 0.830 ± 0.042 | 0.829 ± 0.061 | 0.823 ± 0.072 | 0.830 ± 0.055 | 0.825 ± 0.073 | 0.828 ± 0.052 | 0.828 ± 0.081 | 10 | 0.815 ± 0.056 | 0.825 ± 0.048 | 0.818 ± 0.065 | 0.829 ± 0.061 | 0.824 ± 0.074 | 0.826 ± 0.014 | 0.827 ± 0.099 |
| WDBC | 0 | 0.937 ± 0.036 | 0.955 ± 0.019 | 0.956 ± 0.023 | 0.946 ± 0.034 | 0.952 ± 0.034 | 0.937 ± 0.028 | 0.937 ± 0.036 | 5 | 0.928 ± 0.032 | 0.933 ± 0.041 | 0.951 ± 0.020 | 0.941 ± 0.037 | 0.952 ± 0.029 | 0.936 ± 0.071 | 0.936 ± 0.111 | 10 | 0.920 ± 0.027 | 0.927 ± 0.021 | 0.949 ± 0.038 | 0.940 ± 0.034 | 0.951 ± 0.067 | 0.935 ± 0.083 | 0.935 ± 0.027 |
| WPBC | 0 | 0.757 ± 0.050 | 0.727 ± 0.083 | 0.721 ± 0.101 | 0.711 ± 0.095 | 0.721 ± 0.082 | 0.726 ± 0.101 | 0.757 ± 0.050 | 5 | 0.721 ± 0.087 | 0.721 ± 0.066 | 0.712 ± 0.114 | 0.711 ± 0.098 | 0.711 ± 0.069 | 0.722 ± 0.081 | 0.751 ± 0.114 | 10 | 0.720 ± 0.089 | 0.720 ± 0.053 | 0.710 ± 0.095 | 0.709 ± 0.108 | 0.706 ± 0.101 | 0.719 ± 0.077 | 0.747 ± 0.093 |
| Sonar | 0 | 0.731 ± 0.067 | 0.774 ± 0.060 | 0.779 ± 0.083 | 0.769 ± 0.098 | 0.769 ± 0.098 | 0.731 ± 0.067 | 0.780 ± 0.091 | 5 | 0.730 ± 0.063 | 0.750 ± 0.055 | 0.763 ± 0.064 | 0.765 ± 0.106 | 0.764 ± 0.077 | 0.729 ± 0.013 | 0.772 ± 0.123 | 10 | 0.728 ± 0.067 | 0.744 ± 0.089 | 0.726 ± 0.070 | 0.726 ± 0.075 | 0.730 ± 0.059 | 0.728 ± 0.088 | 0.761 ± 0.105 |
| Average | | 0.798 ± 0.072 | 0.805 ± 0.069 | 0.804 ± 0.074 | 0.804 ± 0.074 | 0.804 ± 0.080 | 0.804 ± 0.068 | 0.812 ± 0.073 |
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