Abstract
Variables selection is a vital Data Mining technique which is used to select the cost-effective predictors by discarding variables with little or no predictive power.
In this paper, we introduce a new conceptual model for variables selection which includes subset generation, Ensemble learning, models selection and validation. Particularly, we addressed the problem of searching for and discarding irrelevant variables, scoring variables by relevance and selecting a subset of the cost-effective predictors. The generalization was seen to improve significantly in terms of recognition accuracy when the proposed system, which is named SPAS, is tested on QoS for Real-World Web Services. Good experimental studies demonstrate the effectiveness of our Wrapper model.
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References
Smith, M.S., Fahrmeir, L.: Spatial Bayesian variable selection with application to functional magnetic resonance imaging. J. Am. Stat. Assoc. 102(478), 417–431 (2007). Taylor & Francis
Long, N., Gianola, D., Rosa, G.J.M., Weigel, K.A.: Dimension reduction and variable selection for genomic selection: application to predicting milk yield in Holsteins. J. Anim. Breed. Genet. 128(4), 247–257 (2011). Wiley Online Library
Chagas, C.M., Koike, C.: Bayesian Approach to Action Selection and Attention Focusing: An Application in Autonomous Robot Programming. Institut National Polytechnique de Grenoble-INPG (2005)
Lloyd, G.R., Wongravee, K., Silwood, C.J.L., Grootveld, M., Brereton, R.G.: Self organising maps for variable selection: application to human saliva analysed by nuclear magnetic resonance spectroscopy to investigate the effect of an oral healthcare product. Chemometr. Intell. Lab. Syst. 98(2), 149–161 (2009). Elsevier
Zhang, Y., Lee, W., Huang, Y.A.: Intrusion detection techniques for mobile wireless networks. Wireless Netw. 9(5), 545–556 (2003). Springer, New York
Deepa, T., Punithavalli, M.: A GLFES and DFT technique for feature selection in high-dimensional imbalanced dataset. IJCSE 3(2), 336–343 (2012)
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014). Elsevier
Panthong, R., Srivihok, A.: Wrapper feature subset selection for dimension reduction based on ensemble learning algorithm. Procedia Comput. Sci. 72, 162–169 (2015). Elsevier
Lin, C.-J.: Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19(10), 2756–2779 (2007). MIT Press
Kabir, M.M., Islam, M.M., Murase, K.: A new Wrapper feature selection approach using neural network. Neurocomputing 73(16), 3273–3283 (2010). Elsevier
Liu, Y., Zheng, Y.F.: FS_SFS: a novel feature selection method for support vector machines. Pattern Recogn. 39(7), 1333–1345 (2006). Elsevier
Kabir, M.M., Shahjahan, M., Murase, K.: A new hybrid ant colony optimization algorithm for feature selection. Expert Syst. Appl. 39, 3747–3763 (2012). Elsevier
Lu, C.-J., Tsai, D.-M.: Automatic defect inspection for LCDs using singular value decomposition. Int. J. Adv. Manuf. Technol. 25(1–2), 53–61 (2005). Springer
Mladenić, D., Grobelnik, M.: Feature selection on hierarchy of web documents. Decis. Support Syst. 35(1), 45–87 (2003). Elsevier
Amiri, F., Yousefi, M.R., Lucas, C., Shakery, A., Yazdani, N.: Mutual information-based feature selection for intrusion detection systems. J. Netw. Comput. Appl. 34(4), 1184–1199 (2011). Elsevier
Azevedo, A.: Integration of Data Mining in Business Intelligence Systems. IGI Global, Hershey (2014)
Jovic, A., Brkić, K., Bogunovic, N.: A review of feature selection methods with applications. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1200–1205. IEEE (2015)
Jingu, K., Haesun, P.: Sparse nonnegative matrix factorization for clustering. Georgia Institute of Technology (2008)
Hoi, S.C.H., Wang, J., Zhao, P., Jin, R.: Online feature selection for mining big data. In: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, pp. 93–100. ACM (2012)
Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Bordello, G., Hemingway, B., et al.: The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput. 7(2), 32–41 (2008). IEEE
Xia, J., Nick, P., Young, N., Wishart, D.S.: MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 37(suppl 2), W652–W660 (2009). Oxford Univ. Press
Kaur, P.D., Chana, I.: Cloud based intelligent system for delivering health care as a service. Comput. Methods Program. Biomed. 113(1), 346–359 (2014)
Zhao, J., Wang, G.-Y., Wu, Z.-F., Tang, H., Li, H.: The study on technologies for feature selection. In: Proceedings of the 2002 International Conference on Machine Learning and Cybernetics 2002, vol. 2, pp. 689–693. IEEE (2002)
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Djellali, C., Adda, M. (2017). A New Scalable and Performance-Enhancing Bootstrap Aggregating Scheme for Variables Selection. In: Aïmeur, E., Ruhi, U., Weiss, M. (eds) E-Technologies: Embracing the Internet of Things . MCETECH 2017. Lecture Notes in Business Information Processing, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-319-59041-7_14
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DOI: https://doi.org/10.1007/978-3-319-59041-7_14
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