ECML '95 Proceedings of the 8th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
Computational Optimization and Applications
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
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Prediction of liquefaction is an important subject in geotechnical engineering. Prediction of liquefaction is also a complex problem as it depends on many different physical factors, and the relations between these factors are highly non-linear and complex. Several approaches have been proposed in the literature for modeling and prediction of liquefaction. Most of these approaches are based on classical statistical approaches and neural networks. In this paper a new approach which is based on classification data mining is proposed first time in the literature for liquefaction prediction. The proposed approach is based on extracting accurate classification rules from neural networks via ant colony optimization. The extracted classification rules are in the form of IF-THEN rules which can be easily understood by human. The proposed algorithm is also compared with several other data mining algorithms. It is shown that the proposed algorithm is very effective and accurate in prediction of liquefaction.