Abstract
Feature selection, i.e., Attribute reduction, is one of the most important applications of fuzzy rough set theory. The application of attribute reduction based on fuzzy rough set is inefficient or even unfeasible on large scale data. Considering the random sampling technique is an effective method to statistically reduce the calculation on large scale data, we introduce it into the fuzzy rough based feature selection algorithm. This paper thus proposes a random reduction algorithm based on random sampling. The main contribution of this paper is the introduction of the idea of random sampling in the selection of attributes based on minimum redundancy and maximum correlation. First, in each iteration the significance of attribute is not computed on all the objects in the whole datasets, but on part of randomly selected objects. By this way, the maximum relevant attribute is chosen on the condition of less calculation. Secondly, in the process of choosing attribute in each iteration, the sample is different so as to select the minimum redundancy attribute. Finally, the experimental results show that the reduction algorithm can obviously reduce the running time of the reduction algorithm on the condition of limited classification accuracy loss.
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References
Bluma, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97, 245–271 (1997)
Chen, H.M., Li, T.R., Ruan, D., Lin, J.H., Hu, C.X.: A rough-set based incremental approach for updating approximations under dynamic maintenance, environments. IEEE Trans. Knowl. Data Eng. 25, 274–284 (2013)
Chen, D.G., Wang, X.Z., Yeung, D.S., Tsang, E.C.C.: Rough approximations on a complete completely distributive lattice with applications to generalized rough sets. Inf. Sci. 176, 1829–1848 (2006)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17, 191–208 (1990)
Hnich, B., Rossi, R., Tarim, S.A., Prestwich, S.: Filtering algorithms for global chance constraints. Artif. Intell. 189, 69–94 (2012)
Hu, Q.H., Zhang, L., An, S., Zhang, D., Yu, D.R.: On robust fuzzy rough set models. IEEE Trans. Fuzzy Syst. 20(4), 636–651 (2012)
Hu, Q.H., Yu, D.R., Xie, Z.X.: Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn. Lett. 27, 414–423 (2006)
Jensen, R., Shen, Q.: Fuzzy-rough attributes reduction with application to web categorization. Fuzzy Sets Syst. 141, 469–485 (2004)
Joshi, S., Jermaine, C.: Materialized sample views for database approximation. IEEE Trans. Knowl. Data Eng. 20(3), 337–351 (2008)
Karabadji, N.E., Seridi, H., Khelf, I., Azizi, N., Boulkroune, R.: Improved decision tree construction based on attribute selection and data sampling for fault diagnosis in rotating machines. Eng. Appl. Artif. Intell. 35, 71–83 (2014)
Léon, B., Olivier, B.: The tradeoffs of large scale learning. In: Advances in Neural Information Processing Systems, pp. 161–168 (2008)
Liang, J.Y., Wang, F., Dang, C.Y., Qian, Y.H.: A group incremental approach to feature selection applying rough set technique. IEEE Trans. Knowl. Data Eng. 26(2), 294–308 (2014)
Motwani, R., Raghavan, P.: Randomized Algorithms. Cambridge University Press, Cambridge (1995)
Provost, F., Jensen, D., Oates, T.: Efficient progressive sampling. In: Proceedings of KDD 1999, pp. 23–32 (1999)
Qian, Y.H., Liang, J.Y., Pedryc, W., Dang, C.Y.: Positive approximation: an accelerator for attribute reduction in rough set theory. Artif. Intell. 174, 597–618 (2010)
Tarim, S.A., Manandhar, S., Walsh, T.: Stochastic constraint programming: ascenario-based approach. Constraints 11, 53–80 (2006)
Dai, J., Hu, H., Wu, W.Z., et al.: Maximal discernibility pairs based approach to attribute reduction in fuzzy rough sets. IEEE Trans. Fuzzy Syst. PP(99), 1 (2017)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Liu, Y., Zhao, S., Chen, H., Li, C., Lu, Y.: Fuzzy rough incremental attribute reduction applying dependency measures. In: Chen, L., Jensen, C.S., Shahabi, C., Yang, X., Lian, X. (eds.) APWeb-WAIM 2017. LNCS, vol. 10366, pp. 484–492. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63579-8_37
Zhang, L., Suganthan, P.N.: A survey of randomized algorithms for training neural networks. Inf. Sci. 364, 146–155 (2016)
Ott, R.L., Longnecker, M.T.: An introduction to statistical methods and data analysis. Nelson Education (2015)
Wen, X., Shao, L., Xue, Y., Fang, W.: A rapid learning algorithm for vehicle classification. Inf. Sci. 295, 395–406 (2015)
Anagnostopoulos, E., Emiris, I.Z., Psarros, I.: Randomized embeddings with slack, and high-dimensional Approximate Nearest Neighbor. Comput. Sci. (2016)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11, 341–356 (1982)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Thisted, R.A.: Elements of statistical computing: Numerical computation. Routledge, New York (2017)
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Zhenlei, W., Suyun, Z., Yangming, L., Hong, C., Cuiping, L., Xiran, S. (2018). Fuzzy Rough Based Feature Selection by Using Random Sampling. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_11
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DOI: https://doi.org/10.1007/978-3-319-97310-4_11
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