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.

[1]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[2]  Xin Yao,et al.  A novel evolutionary data mining algorithm with applications to churn prediction , 2003, IEEE Trans. Evol. Comput..

[3]  Alex Alves Freitas,et al.  Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[4]  Yupu Yang,et al.  Quantum-Inspired Differential Evolution for Binary Optimization , 2008, 2008 Fourth International Conference on Natural Computation.

[5]  Peter Clark,et al.  The CN2 induction algorithm , 2004, Machine Learning.

[6]  Andries Petrus Engelbrecht,et al.  Binary Differential Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[7]  Stephen F. Smith,et al.  Flexible Learning of Problem Solving Heuristics Through Adaptive Search , 1983, IJCAI.

[8]  Chaochang Chiu,et al.  A case-based customer classification approach for direct marketing , 2002, Expert Syst. Appl..

[9]  J. Tin-Yau Kwok,et al.  An extended genetic rule induction algorithm , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[10]  Douglas B. Kell,et al.  Explanatory Analysis of the Metabolome Using Genetic Programming of Simple, Interpretable Rules , 2000, Genetic Programming and Evolvable Machines.

[11]  Alex A. Freitas,et al.  A hybrid PSO/ACO algorithm for discovering classification rules in data mining , 2008 .

[12]  H.S. Lopes,et al.  A parallel genetic algorithm for rule discovery in large databases , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[13]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[14]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[15]  Alex A. Freitas,et al.  Discovering comprehensible classification rules with a genetic algorithm , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[16]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[17]  Tiago Ferra de Sousa,et al.  Particle Swarm based Data Mining Algorithms for classification tasks , 2004, Parallel Comput..

[18]  Ivanoe De Falco,et al.  Discovering interesting classification rules with genetic programming , 2002, Appl. Soft Comput..

[19]  Jong-Hwan Kim,et al.  Quantum-Inspired Evolutionary Algorithms With a New Termination Criterion , H Gate , and Two-Phase Scheme , 2009 .

[20]  Ivan Zelinka,et al.  MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 1: the optimization method , 2004 .

[21]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithms with a new termination criterion, H/sub /spl epsi// gate, and two-phase scheme , 2004, IEEE Transactions on Evolutionary Computation.

[22]  Jing Liu,et al.  An organizational coevolutionary algorithm for classification , 2006, IEEE Trans. Evol. Comput..

[23]  Martine Collard,et al.  Evolutionary data mining: an overview of genetic-based algorithms , 2001, ETFA 2001. 8th International Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.01TH8597).

[24]  Kenneth A. De Jong,et al.  Using genetic algorithms for concept learning , 1993, Machine Learning.