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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
  1. The polar narcosis model label was defined as the positive class.