Table 6 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 | Falsenegative | Accuracy |
|---|---|---|---|---|
SMO | 296 | 47(12%) | 33(8.7%) | 0.787 |
Part | 303 | 34(9%) | 39(10.3%) | 0.805 |
NaiveBayes | 281 | 67(17%) | 28(7.4%) | 0.747 |
J48 | 296 | 44(11.7%) | 36(9.5%) | 0.787 |
IBK(1) | 307 | 42(11.1%) | 27(7.1%) | 0.816 |
IBK(3) | 300 | 42(11.1%) | 34(9%) | 0.797 |
IBK(5) | 299 | 46(12.2%) | 31(8.2%) | 0.795 |
BayesNet | 273 | 76(20.1%) | 27(7.1%) | 0.726 |
DMS | 297 | 48(12.7%) | 31(8.2%) | 0.719 |
3-CPMI | 316 | 29 (7.7%) | 31(8.2%) | 0.844 |
5-CPMI | 305 | 33(8.7%) | 38(10.1%) | 0.811 |
10-CPMI | 288 | 41(10.9%) | 47(12.5 %) | 0.766 |
3-APMI | 306 | 33(8.7%) | 37(9.8%) | 0.813 |
5-APMI | 300 | 41(10.9%) | 35(9.3%) | 0.797 |