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2017 | OriginalPaper | Chapter

An Optimal Multi-view Ensemble Learning for High Dimensional Data Classification Using Constrained Particle Swarm Optimization

Authors : Vipin Kumar, Sonajharia Minz

Published in: Information, Communication and Computing Technology

Publisher: Springer Singapore

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Abstract

Multiple views of a dataset are constructed using the Feature Set Partitioning (FSP) methods for Multi-view Ensemble Learning (MEL). The way of partitioning of features influences the classification performance of MEL. The possible numbers of features set partition of the dataset are equal to the Bell number, which is in polynomial nature and a NP-hard problem (Shown in Fig. 1). It is essential to find an optimal classification performance of MEL for a features set partition among all possible features set partition in high dimension scenario. Therefore, an optimal multi-view ensemble learning approach using constrained particle swarm optimization method (OMEL-C-PSO) is proposed for high dimensional data classification. The experiments have been performed on ten high dimensional datasets. Using four exiting features set partitioning methods; the result shows that OMEL-C-PSO approach is feasible and effective for high dimensional data classification.

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Literature
1.
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. Learning (cs.LG) (2013)
2.
Kakade, S.M., Foster, D.P.: Multi-view regression via canonical correlation analysis. In: COLT (2007)
3.
Ho, T.K.: Nearest neighbors in random subspaces. In: Proceedings of the Second International Workshop on Statistical Techniques in Pattern Recognition, Sydney, Australia, pp. 640–648 (1998)
4.
Bay, S.: Nearestneighbour classification from multiple feature subsets. Intell. Data Anal. 3(3), 191–209 (1999)CrossRef
5.
Bryll, R., Gutierrez-Osunaa, R., Quek, F.: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recogn. 36, 1291–1302 (2003)CrossRefMATH
6.
Cunningham, P., Carney, J.: Diversity Versus Quality in Classification Ensembles Based on Feature Selection. In: López de Mántaras, R., Plaza, E. (eds.) ECML 2000. LNCS, vol. 1810, pp. 109–116. Springer, Heidelberg (2000). doi:10.​1007/​3-540-45164-1_​12 CrossRef
7.
Zenobi, G., Cunningham, P.: Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. In: Proceedings of the European Conference on Machine Learning (2001)
8.
Wu, Q.X., Bell, D., McGinnity, M.: Multi-knowledge for decision making. Knowl. Inf. Syst. 7(2005), 246–266 (2005)CrossRef
9.
Hu, Q.-H., Yu, D.-R., Wang, M.-Y.: Constructing Rough Decision Forests. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds.) RSFDGrC 2005. LNCS, vol. 3642, pp. 147–156. Springer, Heidelberg (2005). doi:10.​1007/​11548706_​16 CrossRef
10.
Bao, Y., Ishii, N.: Combining Multiple K-Nearest Neighbor Classifiers for Text Classification by Reducts. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 340–347. Springer, Heidelberg (2002). doi:10.​1007/​3-540-36182-0_​34 CrossRef
11.
Rodriguez, J.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 20(10), 1619–1630 (2006)CrossRef
12.
Rokach, L., Maimon, O., Arad, O.: Improving supervised learning by sample decomposition. Int. J. Comput. Intell. Appl. 5(1), 37–54 (2005)CrossRef
13.
Rokach, L.: Pattern Classification Using Ensemble Methods. Series in Machine Perception and Artifical Intelligence, vol. 75. World Scientific Publishing Company, Singapore (2010)
14.
Kudo, M., Sklansky, J.: A Comparative evaluation of medium and large-scale feature selectors for pattern classifiers. In: Proceedings of the 1st International Workshop on Statistical Techniques in Pattern Recognition, Prague, Czech Republic, pp. 91–96 (1997)
15.
Bluma, A.L., Langley, P.: Selection of relevant features and examples in machine learning. In: Greiner, R., Subramanian, D. (eds.) Artificial Intelligence on Relevance, Artificial Intelligence, vol. 97, pp. 245–271 (1997)
16.
Brefeld, U., Gartner, C., Scheffe, T.: Multi-view Discriminative sequential learning. Machine Learning. In: ECML 2005, pp. 60–71 (2005)
17.
Almuallim, H., Dietterich, T.G.: Learning Boolean concepts in the presence of many irrelevant features. Artif. Intell. 69(1–2), 279–305 (1994)MathSciNetCrossRefMATH
18.
Ben-Bassat, M.: Pattern recognition and reduction of dimensionality. In: Krishnaiah, P.R., Kanal, L.N. (eds.) Handbook of statistics-II, pp. 773–791. Elsevier, Amsterdam (1982)
19.
Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 359–366 (2000)
20.
Eberhart, R., Kennedy, J.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
21.
Valle, Y., Venayaganmoorthy, G.K., Mohagheghi, S., Hernandez Ronald, J., Harley, G.: Particle swarm optimization: basic concepts, variants and applications in power system. IEEE Trans. Evolut. Comput. 12(2), 171–195 (2008)CrossRef
22.
Engelbrecht, A.P.: Particle swarm optimization: Where does it belong? In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 48–54, May (2006)
23.
Bai, Q.: Analysis of particle swarm optimization algorithm. Comput. Inf. Sci. 3, 180–184 (2010)
24.
Jordehi, A.R., Jasni, J.: Heuristic methods for solution of FACTS optimization problem in power systems. IEEE SCRD 2011, 30–35 (2011)
25.
Chen,B., Feng, X.: CSV-PSO and its application in geotechnical engineering. Swarm Intelligence, Focus on Ant and Particle Swarm Optimization (2007)
26.
Zhang, Y., Gallipoli, D., Augarde, C.: Parallel Hybrid Particle Swarm Optimization and Applications in Geotechnical Engineering. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. LNCS, vol. 5821, pp. 466–475. Springer, Heidelberg (2009). doi:10.​1007/​978-3-642-04843-2_​49 CrossRef
27.
Hsieh, S., Sun, T., Liu, C., Lin, C.: An improved particle swarm optimizer for placement constraints. J. Artif. Evolut. Appl. 13, (2008)
28.
Al Rashidi, M.R., AlHajri, M.F., Al-Othman, A.K., El-Naggar, K.M.: Particle swarm optimization and its applications in power systems. Comput. Intell. Power Eng. 302, 295–324 (2010)CrossRef
29.
Singh, N., Arya, R., Agrawal, R.K.: A novel approach to combine features for salient object detection using constrained particle swarm optimization. Pattern Recogn. 47, 1731–1739 (2014)CrossRef
30.
Sapankevych, N.I., Sankar, R.: Constrained motion particle swarm optimization and support vector regression for non-linear time series regression and prediction applications. In: 12th International Conference on Machine Learning and Applications (ICMLA), vol. 2. IEEE (2013)
31.
32.
Kusiak, A.: Decomposition in Data Mining: An Industrial Case Study. IEEE Trans. Electron. Packag. Manuf. 23(4), 345–353 (2000)CrossRef
33.
Liao, Y., Moody, J.: Constructing heterogeneous committees via input feature grouping. In: Solla, S.A., Leen, T.K., Muller, K.-R. (eds.) Advances in neural information processing systems, vol. 12, pp. 921–927. MIT Press, Cambridge (2000)
34.
Tumer, K., Ghosh, J.: Error correlation and error reduction in ensemble classifiers. Connect. Sci. 8(3–4), 385–404 (1996)CrossRef
35.
Rokach, L.: Genetic algorithm-based feature set partitioning for classification problems. Pattern Recogn. 41(5), 1676–1700 (2008)CrossRefMATH
36.
Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Diversity in search strategies for ensemble feature selection. Inf. Fusion 6(1), 83–98 (2005)CrossRef
37.
Gunter, S., Bunke, H.: Feature selection algorithms for the generation of multiple classifier systems. Pattern Recogn. Lett. 25(11), 1323–1336 (2004)CrossRef
38.
Sun, S., Jin, F. Tu, W.: View construction for multi-view semi-supervised learning. Advances in Neural Networks–ISNN 2011, pp. 595–60 (2011)
39.
Ho, T.K.: The random subspace method for constructing decision forests. Pattern Anal. Mach. Intell. IEEE Trans. 20(8), 832–844 (1998)CrossRef
40.
Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. Pattern Anal. Mach. Intell. IEEE Trans. 28(7), 1088–1099 (2006)CrossRef
41.
Floudas, C.A., Pardalos, P.M.: A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1990). doi:10.​1007/​3-540-53032-0 MATH
42.
Parsopoulos, K., Vrahatis, M.: Particle swarm optimization method for con- strained optimization problems. Intell. Technol. 16(2002), 214–220 (2002)MATH
43.
Yang, J.-M., Chen, Y.-P., Horng, J.-T., Kao, C.-Y.: Applying family competition to evolution strategies for constrained optimization. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 201–211. Springer, Heidelberg (1997). doi:10.​1007/​BFb0014812 CrossRef
44.
Homaifar, A., Lai, A.H.Y., Qi, X.: Constrained optimization via genetic algorithms. Simulation 2(4), 242–254 (1994)CrossRef
45.
Ahmed, A., Esmin, A., Coelho, R.A., Matwin, S.: A review on particle swarm optimization algorithm and its variant to clustering high- dimensional data. Artif. Intel. Rev. 44(1), 23–45 (2013)
46.
Kennedy, J.: The behavior of particles. Evol. Progr. VII(1998), 581–587 (1998)
47.
Clerc, M.: The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. In: Congress on Evolutionary Computation (CEC 1999), pp. 1951–1957 (1999)
48.
Opitz, D.: Feature Selection for Ensembles. In: Proceedings 16th National Conference on Artificial Intelligence. AAAI, pp. 379–384 (1999)
49.
50.
51.
52.
53.
Minz S., Kumar, V.: reinforced multi-view ensemble learning for high dimensional data classification. In: International Conference on Communication and Computing (ICC 2014), Elsevier (2014)
54.
Kumar, V., Minz, S.: Multi-view Ensemble Learning for Poem Data Classification Using SentiWordNet. In: Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.) Advanced Computing, Networking and Informatics- Volume 1. SIST, vol. 27, pp. 57–66. Springer, Cham (2014). doi:10.​1007/​978-3-319-07353-8_​8 CrossRef
55.
Kumar, V., Minz, S.: Feature selection: a literature review. Smart Comput. Rev. KAIS 4(3), 211–229 (2014)
56.
Breiman, L.: Bagging predictor. Mach. Learn. 24, 123–140 (1996)MATH
57.
Sun, S., Jin, F., Tu, W.: View construction for multi-view semi-supervised learning. Advances in Neural Networks–ISNN 2011, pp. 595–601 (2011)
58.
Branson, D.: Stirling numbers and Bell numbers: their role in combinatoricsand probability. Math. Sci. 25, 1–31 (2000)MathSciNetMATH
59.
60.
Garcia, S., Herrera, F.: An extension on statistical comparison of classifiers over multiple datasets for all pair-wise comparisons. Mach. Learn. Res. 09, 2677–2694 (2008)MATH
61.
Kumar, V., Minz, S.: Multi-view ensemble learning: an optimal feature set partitioning for high dimensional data classification. Knowl. Inf. Syst. 49(01), 1–59 (2015)CrossRef
Metadata
Title
An Optimal Multi-view Ensemble Learning for High Dimensional Data Classification Using Constrained Particle Swarm Optimization
Authors
Vipin Kumar
Sonajharia Minz
Copyright Year
2017
Publisher
Springer Singapore
Book
Information, Communication and Computing Technology

Print ISBN: 978-981-10-6543-9

Electronic ISBN: 978-981-10-6544-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-6544-6_33

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