From: Harnessing ontology and machine learning for RSO classification
EI | M&D | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R-OBC | J48 | BayesNet | |||||||||||
O[1] | O[2] | R | S | A | O | R | S | A | O | R | S | A | |
Accuracy | 0.872 | 0.85 | 0.893 | 0.892 | 0.884 | 0.854 | 0.837 | 0.707 | 0.848 | 0.899 | 0.862 | 0.862 | 0.878 |
(W)FP rate | 0.03 | 0.041 | 0.033 | 0.032 | 0.031 | 0.124 | 0.157 | 0.437 | 0.119 | 0.027 | 0.14 | 0.137 | 0.085 |
(W)precision | 0.891 | 0.87 | 0.892 | 0.892 | 0.891 | 0.809 | 0.79 | 0.609 | 0.807 | 0.904 | 0.862 | 0.864 | 0.876 |
(W)recall | 0.891 | 0.87 | 0.894 | 0.893 | 0.892 | 0.854 | 0.837 | 0.707 | 0.848 | 0.899 | 0.862 | 0.862 | 0.878 |
T (s) | 0.87 | 4.1 | 0.36 | 0.29 | 0.46 | 3.74 | 3.58 | 3.64 | 3.95 | 0.27 | 0.11 | 0.11 | 0.13 |
EI | M&D | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
JRip | SMO | RandomForest (50-trees) | ||||||||||
O | R | S | A | O | R | S | A | O | R | S | A | |
Accuracy | 0.92 | 0.864 | 0.817 | 0.838 | 0.903 | 0.888 | 0.395 | 0.903 | 0.9 | 0.889 | 0.881 | 0.893 |
(W)FP rate | 0.035 | 0.084 | 0.228 | 0.191 | 0.04 | 0.044 | 0.251 | 0.041 | 0.082 | 0.103 | 0.136 | 0.082 |
(W)precision | 0.918 | 0.847 | 0.816 | 0.843 | 0.902 | 0.886 | 0.527 | 0.902 | 0.897 | 0.888 | 0.879 | 0.892 |
(W)recall | 0.92 | 0.864 | 0.817 | 0.838 | 0.903 | 0.888 | 0.395 | 0.903 | 0.9 | 0.889 | 0.881 | 0.893 |
T (s) | 3.7 | 3.23 | 3.31 | 3.64 | 3.67 | 3.47 | 3.65 | 3.70 | 313.52 | 271.91 | 277.67 | 297.64 |
EI | M&D | |||||||
---|---|---|---|---|---|---|---|---|
MultilayerPerceptron (4-hidden-layer) | SimpleLogistic | |||||||
O | R | S | A | O | R | S | A | |
Accuracy | 0.847 | 0.46 | 0.155 | 0.740 | 0.9 | 0.897 | 0.746 | 0.742 |
(W)FP rate | 0.044 | 0.084 | 0.085 | 0.223 | 0.039 | 0.048 | 0.267 | 0.373 |
(W)precision | 0.813 | 0.691 | 0.667 | 0.668 | 0.899 | 0.901 | 0.683 | 0.635 |
(W)recall | 0.847 | 0.46 | 0.155 | 0.740 | 0.9 | 0.897 | 0.746 | 0.742 |
T (s) | 294.83 | 342.32 | 308.75 | 315.10 | 3532.5 | 3440.7 | 3479 | 3643.57 |