International Journal of Soft Computing

Year: 2011
Volume: 6
Issue: 2
Page No. 20 - 25

An Evolutionary Multi Label Classification Using Associative Rule Mining

Authors : J. Arunadevi and V. Rajamani

References

Beckers, R., J.L. Deneubourg and S. Goss, 1992. Trails and U-turns in the selection of a path by the ant Lasius niger. J. Theor. Biol., 159: 397-415.
Direct Link  |  

Brinker, K., J. Furnkranz and E. Hullermeier, 2006. A unified model for multilabel classification and ranking. Proceedings of the 17th European Conference on Artificial Intelligence, Aug. 29-Sept. 1, Italy, pp: 489-493.

Chen, B., L. Ma and J. Hu, 2010. An improved multi-label classification method based on svm with delicate decision boundary. Int. J. Innovative Comp. Inform. Control, 6: 1605-1614.

Cheng, W. and E. Hullermeier, 2009. Combining instance-based learning and logistic regression for multilabel classification. Mach. Learn., 76: 211-225.
CrossRef  |  

Corcoran, L. and S. Sen, 1994. Using real-valued genetic algorithms to evolve rule sets for classification. Proceedings of the 1st IEEE Conference on Evolutionary Computation, June 1994, Orlando, FL. USA., pp: 120-124.

Dehuri, S. and R. Mall, 2006. Predictive and comprehensible rule discovery using a multi-objective genetic algorithm. Knowledge Based Syst., 19: 413-421.
CrossRef  |  

Dehuri, S. and S.B. Cho, 2008. Multi-objective classification rule mining using gene expression programming. Proceedings of the 3rd International Conference on Convergence and Hybrid Information Technology, Nov. 11-13, Busan, pp: 754-760.

Dehuri, S., S. Ghosh and A. Ghosh, 2008. Genetic Algorithms for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases. In: Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, Ghosh, A. (Eds.). Springer, Heidelberg, pp: 1-22.

Dehuri, S., S. Patnaik, A. Ghosh and R. Mall, 2008. Application of elitist multi-objective genetic algorithm for classification rule generation. Applied Soft Comput., 8: 477-487.
CrossRef  |  

Dorigo, M., 1992. Optimization, learning and natural algorithms. Ph.D. Thesis, Department of Electronics, Polytechnic University of Milan, Italy.

Dorigo, M., V. Maniezzo and A. Colorni, 1991. Positive feedback as a search strategy. Technical Report No. 91-016, Politecnico di Milano, Italy.

Dorigo, M., V. Maniezzo and A. Colorni, 1991. The ant system: An autocatalytic optimizing process. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Milano, Italy.

Ester, M., H.P. Kriegel and J. Sander, 1997. Spatial data mining: A database approach. Proceedings of the 5th International Symposium on Advances in Spatial Databases, (ASD'97), Springer-Verlag, Berlin, pp: 47-66.

Fayyad, U., G. Piatetsky-Shapiro, G. Smith and R. Uthurusamy, 1998. Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, California.

Frank, R., M. Ester and A. Knobbe, 2009. A multi-relational approach to spatial classification. Proceedings of the 15th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, June 28-July 01, Paris, France, pp: 309-318.

Ghamrawi, N. and A. McCallum, 2005. Collective multi-label classification. Proceedings of the 14th ACM International Conference on Information and Knowledge management, Oct. 31-Nov. 5, Bremen, Germany, pp: 195-200.

Goss, S., S. Aron, J.L. Deneubourg and J.M. Pasteels, 1989. Self-organized shortcuts in the argentine ant. Naturwissenschaften, 76: 579-581.
CrossRef  |  

Holldobler, B. and E.O. Wilson, 1990. The Ants. 1st Edn., Springer-Verlag, Berlin, Heidelberg, ISBN: 978-3-540-52092-4, pp: 10-55.

Koperski, K., 1999. Progressive refinement approach to spatial data mining. Ph.D. Thesis, Computing Science, Simon Fraser University.

Koperski, K., J. Han and N. Stefanovic, 1998. An efficient two-step method for classification of spatial data. Proceedings of the International Symposium on Spatial Data Handling, (SDH'98), Vancouver, BC Canada, pp: 45-54.

Li, W., J. Han and J. Pei, 2001. CMAR: Accurate and efficient classification based on multiple class-association rules. Proceedings of the 2001 IEEE International Conference on Data Mining, November 29-December 2, 2001, San Jose, CA., pp: 369-376.

Liu, B., W. Hsu and Y. Ma, 1998. Integrating classification and association rule mining. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, August 27-31, 1998, New York, USA., pp: 80-86.

Malerba, D., F. Esposito, A. Lanza, F.A. Lisi and A. Appice, 2003. Empowering a GIS with inductive learning capabilities: The case of INGENS. Comp. Environ. Urban Syst., 27: 265-281.
CrossRef  |  

Mennis, J. and D. Guo, 2009. Spatial data mining and geographic knowledge discovery: An introduction. Comp. Environ. Urban Syst., 33: 403-408.
CrossRef  |  

Shi, X.J. and H. Lei, 2008. A genetic algorithm-based approach for classification rule discovery. Proceedings of the International Conference on Information Management, Innovation Management and Industrial Engineering, Dec. 19-21, Taipei, pp: 175-178.

Thabtah, F., 2006. Challenges and interesting research directions in associative classification. Proceedings of the 6th IEEE International Conference on Data Mining Workshops, Dec. 18-22, Hong Kong, pp: 785-792.

Thabtah, F., P. Cowling and Y. Peng, 2004. MMAC: A new multi-class, multi-label associative classification approach. Proceedings of the 4th International Conference on Data Mining, November 1-4, 2004, Brighton, UK., pp: 217-224.

Thabtah, F., P. Cowling and Y. Peng, 2004. Multi-label classification learning. Proceedings of the IEEE International Conference on Advances in Intelligent Systems, November 2004, Luxembourg, Luxembourg, pp: 207-213.

Tsoumakas, G., I. Katakis and I. Vlahavas, 2009. Mining Multi-Label Data. In: Data Mining and Knowledge Discovery Handbook, Maimon, O. and L. Rokach (Eds.). 2nd Edn., Springer, Heidelberg.

Yin, X. and J. Han, 2003. CPAR: Classification based on predictive association rules. Proceeding of the International Conference on Data Mining, (ICDM'03), San Fransisco, CA., pp: 331-335.

Zhang, M.L. and Z.H. Zhou, 2007. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognit., 40: 2038-2048.
CrossRef  |  

Zheng, X. and L. Zhao, 2008. Association rule analysis of spatial data mining based on matlab-a case of ancheng township in China. Proceedings of the 1st International Workshop on Knowledge Discovery and Data Mining, Jan. 23-24, Adelaide, South Australia, pp: 76-80.

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved