Table 2 Comparison of classification algorithms according to a number of correctly classified elements, false positive, false negative and the classifiers accuracies
From: Using Pareto points for model identification in predictive toxicology
Method | Correct class | Falsepositive | False negative | Accuracy |
|---|---|---|---|---|
SMO | 899 | 122 (10.8%) | 106 (9.4%) | 0.80 |
Part | 904 | 123 (10.9%) | 101 (8.9%) | 0.80 |
NaiveBayes | 845 | 191 (19%) | 90 (7.9%) | 0.75 |
J48 | 905 | 123 (10.9%) | 100 (8.9%) | 0.80 |
IBK(1) | 905 | 121 (10.7%) | 102 (9%) | 0.80 |
IBK(3) | 901 | 133 (11.7%) | 94 (8.3%) | 0.79 |
IBK(5) | 889 | 149 (13.2%) | 93(8.2%) | 0.78 |
BayesNet | 756 | 264 (23%) | 108 (9.5%) | 0.67 |
DMS | 901 | 115 (10.1%) | 112 (9.9%) | 0.79 |
3-CPMI | 902 | 136 (12%) | 90 (7.9%) | 0.79 |
5-CPMI | 897 | 137 (12%) | 94 (8.3%) | 0.79 |
10-CPMI | 863 | 168 (14.8%) | 97 (8.5%) | 0.76 |
3-APMI | 918 | 99 (8.7%) | 111(9.8%) | 0.81 |
5-APMI | 891 | 115 (10%) | 122 (10.8%) | 0.78 |