Ant-based Feature Decomposition Method in Constructing NMC and NBC ensembles

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-30 Number-1
Year of Publication : 2015
Authors : Abdullah
DOI :  10.14445/22312803/IJCTT-V30P108

MLA

Abdullah "Ant-based Feature Decomposition Method in Constructing NMC and NBC ensembles". International Journal of Computer Trends and Technology (IJCTT) V30(1):46-49, December 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Several approaches have been proposed to construct a set of diverse classifiers within an ensemble. One of the approaches is the input features manipulation. Feature decomposition methods are those that manipulate the input feature set in creating the ensemble. However, it is difficult to determine how to partition the feature set into several feature subsets to train base classifiers which may lead to an accurate and diverse ensemble. This paper proposes ant-based feature decomposition method in constructing nearest mean classifier (NMC) ensembles and naïve bayes classifier (NBC) ensembles. Experiments were carried out on several University California, Irvine (UCI) datasets to test the performance of the proposed method. Experimental results showed that the proposed method has successfully constructed better nearest mean classifier (NMC) and naïve bayes classifier (NBC) ensembles.

References
[1] H. Koyuncu, and R. Ceylan, “Artificial neural network based on rotation forest for biomedical pattern classification,” in Proceedings of the 36th International Conference on Telecommunications and signal processing (TSP), 2013, pp. 581-585.
[2] A. Margoosian, and J. Abouei, “Ensemble-based classifiers for cancer classification using human tumor microarray data,” in Proceedings of the 21st International Conference on Electrical Engineering, 2013, pp. 1-6.
[3] U. Turhal, S. Babur, C. Avci, and A. Akbas, “Performance improvement for diagnosis of colon cancer by using ensemble classification methods,” in Proceedings of International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013, pp. 271-275.
[4] S.K. Shukla, and A. Pandey, “Classification of Devnagari Numerals using Multiple Classifier,” International Journal of Computer Trends and Technology (IJCTT), vol. 12, no. 4, pp. 196-200, 2014.
[5] L.I. Kuncheva and C.J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with ensemble accuracy,” Machine Learning, vol. 51, no. 2, pp. 181-207, 2003.
[6] F. Roli,. “Multiple classifier system,” Encyclopedia of Biometrics, eds. S.Z. Li, and A.K. Jain. New York: Springer Science & Business Media, 2009, pp. 981-986.
[7] O. Maimon, and L. Rokach, Decomposition methodology for Knowledge Discovery and Datamining, Berlin, Germany: Springer, 2005.
[8] L. Rokach, Pattern classification using ensemble method, Singapore: World Scientific, 2010.
[9] H. Ahn, H. Moon, M.J. Fazzari, N. Lim, J.J. Chen, and R.L. Kodell, “Classification by ensembles from random partitions of high-dimensional data,” Computational Statistics and Data Analysis, vol. 51, no. 12, pp. 6166- 6179, 2007.
[10] L. Rokach, “Genetic algorithm-based feature set partitioning for classification problems,” Pattern Recognition, vol. 41, no. 5, pp. 1676-1700, 2008.
[11] C.T. Su, C.F. Chang, and J.P. Chiou, “Distribution network reconfiguration for loss reduction by ant colony search algorithm,” Electric Power Systems Research, vol. 75(2-3), pp. 190-199, 2005.
[12] C.F. Chang, “Reconfiguration and capacitor placement for loss reduction of distribution systems by ant colony search algorithm,” IEEE Transactions on Power Systems, vol. 23, no. 4, pp. 1747-1755, 2008.
[13] F. Shang, and Y. Wang, “An ant system optimization QoS routing algorithm for wireless sensor networks,” in Proceedings of the 3rd International Workshop on Advanced Computational Intelligence, 2010, pp. 25-27.
[14] A. Jevtic, D. Andina, A. James, J. Gomez, and M. Jamshidi, “Unmanned aerial vehicle route optimization using ant system algorithm,” in Proceedings of the 5th International Conference on System of Systems Engineering, 2010, pp. 1-6.
[15] R. Rebeiro, and F. Enembreck, “A sociologically inspired heuristic for optimization algorithm: A case study on ant systems,” Expert System with Application, vol. 40, no. 5, pp. 1814-1826, 2013.
[16] A.H. El Bakely, and H.A. Hefny, “Using Ant Algorithm in Green Cloud Computing to Minimize Energy,” International Journal of Computer Trends and Technology (IJCTT), vol. 27, No. 1, pp. 44-50, 2015
[17] B. Crawford, C. Castro, and E. Monfroy, “A new ACO transition rule for set partitioning and covering problems,” in Proceedings of International Conference of Soft Computing and Pattern Recognition, 2009, pp. 426-429.
[18] B. Crawford, R. Soto, E. Monfroy, C. Castro, W. Palma, and F. Paredes, “A hybrid soft computing approach for subset problems,” Mathematical Problems in Engineering, 2013, pp. 1-12.
[19] T.K. Ho, “The random subspace method for constructing decision forest,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, 1998
[20] G. Serpen, and S. Pathical, “Classification in highdimensional feature spaces: Random subsample ensemble,” in Proceedings of IEEE International Conference on Machine Learning and Application, 2009, pp. 740-745.
[21] H. Li, G. Wen, Z. Yu, and T. Zhou, “Random subspaces evidence classifier,” Neurocomputing, vol. 110, pp. 62-69, 2013

Keywords
Feature decomposition, classifier ensemble, ant-system algorithm.