Statistical Second-Order Predicate: A Decision Model in Uncertain Dataset

The use of statistic to deal with the decision making in uncertain environment has been widely applied. On uncertain data that includes some unknown values and noise, related algorithms are asked to have more powerful and robust expression ability of rules. This paper proposes the statistical second-order predicate (S2OP) algorithm which combines the superiorities of statistic and logic for improving accuracy and robustness of decision making. second-order logic (SOL) has comparatively high efficiency and accuracy for this problem. By use of the value of Kullback-Leibler divergence, the algorithm judges the Russell´s paradox of rule set and chooses the rules eliminated for the consistency of machine-readable knowledge base. The results of experiment show that the S2OP improves the accuracy of about 5% and increase the robustness of about 11.7% comparing with J48, BayesNet and Bagging on the waveform data set which includes 5000 instances and has many noises.