Discovery of Classification Rules Using Distributed Genetic Algorithm

Abstract This paper presents a distributed genetic algorithm for the discovery of classification rules. Population is contained in the form of interconnected demes. The local selection and reproduction mechanism is used to evolve the species within demes, and diversity is enhanced by migrating rules among some of the selected demes. Subsumption operator has been finally applied to reduce the complexity of the rule set discovered. The effectiveness of the proposed distributed genetic algorithm for discovering classification rules is evaluated by comparing the results with traditional crowding GA on 10 datasets from the UCI and KEEL repository. The results confirm that the distributed GA discover classification rules with significantly higher predictive accuracy. The influence of migration operator is also analysed with respect to migration rate.

[1]  Alex Alves Freitas.  A Review of evolutionary Algorithms for Data Mining , 2008, Soft Computing for Knowledge Discovery and Data Mining.

[2]  Enrique Alba,et al.  Heterogeneous Computing and Parallel Genetic Algorithms , 2002, J. Parallel Distributed Comput..

[3]  Kamal Kant Bharadwaj.  A parellel genetic algorithm approach for automated discovery of censored production rules , 2007, Artificial Intelligence and Applications.

[4]  Alex A. Freitas,et al.  A Survey of Parallel Data Mining , 1996 .

[5]  T. Hong,et al.  Using the master-slave parallel architecture for genetic-fuzzy data mining , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[6]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[7]  Jesús Alcalá-Fdez,et al.  KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..

[8]  Reda Alhajj,et al.  Discovering Accurate and Interesting Classification Rules Using Genetic Algorithm , 2006, DMIN.

[9]  Dinesh Kumar,et al.  A genetic algorithm with entropy based initial bias for automated rule mining , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).

[10]  Saroj,et al.  Parallel Genetic Algorithm Approach to Automated Discovery of Hierarchical Production Rules , 2009 .

[11]  Enrique Alba,et al.  A survey of parallel distributed genetic algorithms , 1999, Complex..

[12]  Francisco Herrera,et al.  Hybrid Distributed Real-Coded Genetic Algorithms , 1998, PPSN.

[13]  Lior Rokach,et al.  Soft Computing for Knowledge Discovery and Data Mining , 2007 .

[14]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[15]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[16]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[17]  Alex A. Freitas,et al.  Discovering interesting prediction rules with a genetic algorithm , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[18]  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).

[19]  K. G. Srinivasa,et al.  A self-adaptive migration model genetic algorithm for data mining applications , 2007, Inf. Sci..

[20]  Francisco Herrera,et al.  Hierarchical distributed genetic algorithms , 1999 .

[21]  Francisco Herrera,et al.  Gradual distributed real-coded genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[22]  Kamal Kant Bharadwaj,et al.  A parallel genetic programming based intelligent miner for discovery of censored production rules with fuzzy hierarchy , 2010, Expert Syst. Appl..