Classification rule discovery with DE/QDE algorithm

The quantum-inspired differential evolution algorithm (QDE) is a new optimization algorithm in the binary-valued space. The paper proposes the DE/QDE algorithm for the discovery of classification rules. DE/QDE combines the characteristics of the conventional DE algorithm and the QDE algorithm. Based on some strategies of DE and QDE, DE/QDE can directly cope with the continuous, nominal attributes without discretizing the continuous attributes in the preprocessing step. DE/QDE also has specific weight mutation for managing the weight value of the individual encoding. Then DE/QDE is compared with Ant-Miner and CN2 on six problems from the UCI repository datasets. The results indicate that DE/QDE is competitive with Ant-Miner and CN2 in term of the predictive accuracy.