Abstract
Feature selection, also known as variable selection or attribute reduction, is to select a subset relevant features to speedup learning/mining and to improve the learning/mining quality. In the big data era, some feature selection methods have to face the running time problem led by the large-scale data. As a result, in this paper, we try to narrow this gap by proposing a feature selection accelerator. Considering fuzzy rough techniques need no extra expert knowledge, we design the feature selection accelerator based on fuzzy rough reduction techniques. First, we proposed a fuzzy rough accelerator by deleting the learned/discernible instances in the process of feature selection, which decreases the computation and accelerates feature selection. Second, we design a fuzzy rough based feature selection accelerated algorithm. Finally, the numerical experiments demonstrate that the proposed accelerated algorithm could obtain the same reduction results and save much more time, especially on the large-scale datasets.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. JMLR. 3, 1157–1182 (2003)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the 9th International Conference on Machine Learning, pp. 249–256. Morgan Kaufmann, Los Altos (1992)
Peng, H.C., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston (1991)
Pawlak, Z., Grzymala-Busse, J.W., Slowiski, R., Ziako, W.: Rough sets. Commun. ACM 38(11), 89–95 (1995)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17, 109–137 (1990)
Wang, X.Z., Tang, E.C.C., Zhao, S.Y., Chen, D.G.: Learning fuzzy rules from fuzzy samples based on rough set techniques. Inf. Sci. 177, 4493–4514 (2007)
Hu, Q., Yu, D.R., Xie, Z.X.: Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn. Lett. 27(5), 414–423 (2006)
Qian, Y.H., Liang, J.Y., Pedrycz, W., Dang, C.Y.: Positive approximation: an accelerator for feature reduction in rough set theory. Artif. Intell. 174(9), 597–618 (2010)
Qian, Y.H., Wang, Q., Cheng, H.H., Liang, J.Y., Dang, C.Y.: Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst. 258(C), 1–78 (2015)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17(2–3), 191–209 (1990)
Tsang, E.C.C., Chen, D.G., Yeung, D.S., Wang, X.Z., Lee, J.W.T.: Attributes reduction using fuzzy rough sets. IEEE Trans. Fuzzy Syst. 16(5), 1130–1141 (2008)
Yeung, D.S., Chen, D.G., Tsang, E.C.C., Lee, J.W.T., Wang, X.Z.: On the generalization of fuzzy rough sets. IEEE Trans. Fuzzy Syst. 13, 343–361 (2005)
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)
Yao, Y.Y., Zhao, Y., Wang, J.: On reduct construction algorithms. Trans. Comput. Sci. 2, 100–117 (2008)
Coomans, D., Massart, D.L.: Alternative k-nearest neighbour rules in supervised pattern recognition: part 1. K-Nearest neighbour classification by using alternative voting rules. Analytica Chimica Acta 136, 15–27 (1982)
Kryszkiewicz, M., Lasek, P.: FUN: fast discovery of minimal sets of attributes functionally determining a decision attribute. Trans. Rough Sets 9, 76–95 (2008)
Zhao, S.Y., Chen, H., Li, C.P., Zhai, M.Y., Du, X.Y.: RFRR: robust fuzzy rough reduction. IEEE Trans. Fuzzy Syst. 21(5), 825–841 (2013)
Bhatt, R.B., Gopal, M.: On fuzzy rough sets approach to feature selection. Pattern Recogn. Lett. 26(7), 965–975 (2005)
Chen, D.G., Tsang, E.C.C., Zhao, S.Y.: Attributes reduction with fuzzy rough sets. In: IEEE International Conference on Systems, Man, and Cybernetics, Vol. 1, pp. 486–491 (2007)
Zhao, S.Y., Wang, X.Z., Chen, D.G., Tsang, E.C.C.: Nested structure in parameterized rough reduction. Inf. Sci. 248, 130–150 (2013)
Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recogn. Lett. 24(6), 833–849 (2003)
Chen, D.G., Yang, Y.Y.: Attribute reduction for heterogeneous data based on the combination of classical and fuzzy rough set models. IEEE Trans. Fuzzy Syst. 22(5), 1325–1334 (2014)
Chen, D.G., Zhao, S.Y.: Local reduction of decision system with fuzzy rough sets. Fuzzy Sets Syst. 161(13), 1871–1883 (2010)
Acknowledgements
This work is supported by National Key Research & Develop Plan (No. 2016YFB1000702), National Key R&D Program of China (2017YFB1400700), and NSFC under the grant No. 61732006, 61532021, 61772536, 61772537, 61702522 and NSSFC (No. 12\&ZD220), and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (15XNLQ06). It was partially done when the authors worked in SA Center for Big Data Research in RUC. This Center is funded by a Chinese National 111 Project Attracting. This work is also supported by the Macao Science and Technology Development Fund (081/2015/A3).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ni, P., Zhao, S., Chen, H., Li, C. (2019). An Accelerator of Feature Selection Applying a General Fuzzy Rough Model. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-16145-3_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16144-6
Online ISBN: 978-3-030-16145-3
eBook Packages: Computer ScienceComputer Science (R0)