Minig rules of concept drift using genetic algorithm

• Autorzy: Vivekanandan P. , Nedunchezhian R.
• Języki publikacji: EN

Abstrakty

EN

In a database the data concepts changes over time and this phenomenon is called as concept drift. Rules of concept drift describe how the concept changes and sometimes they are interesting and mining those rules becomes more important. CDR tree algorithm is currently used to identify the rules of concept drift. Building a CDR tree becomes a complex process when the domain values of the attributes get increased. Genetic Algorithms are traditionally used for data mining tasks. In this paper, a Genetic Algorithm based approach is proposed for mining the rules of concept drift, which makes the mining task simpler and accurate when compared with the CDR-tree algorithm.

Słowa kluczowe

EN

genetic algorithm; CDR-tree algorithm; rules; data mining

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2011
• Tom: Vol. 1, No. 2
• Strony: 135--145
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Vivekanandan P.

• Dept. of Comp. Sci. and Engg. Park College of Engineering and Technology Coimbatore, India

autor

Nedunchezhian R.

• Dept of Comp. Sci. and Engg. KalaignarKarunanidhi Institute of Technology Coimbatore, India

Bibliografia

• [1] G. Widmer and M. Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning, 23(1):69-101, 1996.
• [2] A. Tsymbal, The problem of concept drift: definitions and related work, Department of Computer Science, PlaceNameTrinity PlaceTypeCol-lege CityplaceDublin, Tech. Rep. TCD-CS-2004-15, 2004.
• [3] H. Wang, W. Fan, P. S. Yu, and J. Han, Mining concept-drifting data streams using ensemble classifiers, in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 226-235 , 2003.
• [4] G. Hulten, L. Spencer, and P. Domingos , Mining time-changing data streams, Proceedings of 7th ACM SIGKDD International Conference on Knowledge and Data Mining, 97-106, San Francisco, CA, ACM Press, 2001.
• [5] Mihai Lazarescu, Svetha Venkatesh and Hai Hung Bui Using Multiple Windows to Track Concept Drift , Intelligent Data Analysis Journal, 8(1), 2004.
• [6] S. U. Guan and F Zhu Collard, An incremental approach to genetic-algorithms based classification. Systems, Man and Cybernetics, Part B, IEEE Transactions, 35(2):227-239, 2005. '
• [7] Chen-I Lee, Cheng-Jung Tsai, Jhe-Hao Wu and Wei-Pang yang, A Decision Tree-Based Approach to Mining the rules of Concept Drift, Fourth International Conference on Fuzzy System and Knowledge discovery, 2007
• [81 K. A. De Jong., W.M. Spears and DF. Gordon, Using genetic algorithms for concept learning, Machine Learning 13:161-188, 1993.
• [9] C. Z. Janikow, A knowledge-intensive genetic algorithm for supervised learning, Machine Learning 13:189-228, 1993.
• [10] D. P. Greene and S. F. Smith, Competition-based induction of decision models from examples, Machine Learning 13:229-257, 1993.
• [11] K. A. De Jong and W. M. Spears, Using genetic algorithm for supervised concept learning , Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, 1990.
• [12] E. Noda, A.A. Freitas and H.S. Lopes, ' 'Discovering interesting prediction rule with a genetic algorithm , Proceedings of the 1999 Congress on Evolutionary Computation, 2, 1999
• [13] I-hui li, I-en liao and Wei-zhi pang, Mining classification rules in the presence of Concept drift with an incremental genetic Algorithm , journal of theoretical and applied information technology, 2008.
• [14] Alex A Freitas, A Survey of Evolutionary Algorithm for Data Mining and Knoweldge Discovery, Advances in evolutionary computing: theory and applications, 819-845, ACM press, 2003.
• [15] Linyu Yang, D. H. Widyantoro, T. Loerger T, J. Yen, An Entropy-based Adaptive Genetic Algorithm for Learning Classification Rules, IEEE pro-ceding of the 2001 congress on Evolutionary com¬puting 2:790-796, 2001.
• [16] Shixi Chen, Haixun Wang, Shuigeng Zhou, Yu P.S., Stop chasing trends: discovering high order models in evolving data, IEEE 24th International Conference on Data Engineering, 2008.
• [17] Y. Yang, X. Wu and X. Zhu, Combining proactive and reactive predictions for data streams, in SIGKDD, 710-714, 2005.
• [18] Jeremy Z. Kolter, Marcus A. Maloof, Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift, IEEE International confer¬ence on Data Mining, 2003.
• [19] R. Agrawal, A. Ghosh, T. Imielinski, B. Iyer and A. Swami, An interval classifier for database mining applications, In proc. of the 18th Conf. on Very Large Databases, August 1992.
• [20] J. H. Holland, Adaptation in natural and artificial systems, PlaceTypeUniversity of Place-NameMichigan Press, CityplaceAnn Arbor, 1975.
• [21] Wilson SW, Classifier fitness based on accuracy. Evolutionary Computation (3):149-17, 1995.
• [22] Freitas A. A., Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag, 2002.
• [23] P. Vivekanandan R.Neduchezian, Mining data streams with concept drifts using genetic algorithm, Artificial Intelligence Review, Springer-Verlag, Accepted Available online, 2011.