  In this paper, we study how to predict the results of LTL model checking
using some machine learning algorithms. Some Kripke structures and LTL formulas
and their model checking results are made up data set. The approaches based on
the Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), and
Logistic Regression (LR) are used to training and prediction. The experiment
results show that the predictive accuracy of the RF, KNN, DT and LR-based
approaches are 97.9%, 98.2%, 97.1% and 98.2%, respectively, as well as the
average computation efficiencies of the RF, KNN, DT and LR-based approaches are
7102500, 598, 4132364 and 5543415 times than that of the existing approach,
respectively, if the length of each LTL formula is 500.
