Abstract In this paper, a new algorithm for classification rule mining is proposed, which is based on binary ant colony optimization algorithm. Aiming at the long computing time, a mutation operator is involved. To avoid the local optima problem, a disaster operator is also introduced. The algorithm is applied to the dataset from UCI machine learning repository, and the result shows that the forecasting accuracy is improved greatly. Moreover, by the mutation operator and disaster operator, the computing time can be effectively saved and the local optima can be avoided.
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2008, Vol. 21 
