Abstract
Traditional attribute reduction based on neighborhood decision error rate aims to reduce the decision errors through selecting valuable attributes. To further improve the performances of the selected attributes in reducts, an ensemble selector is introduced into such framework. Different from the previous strategy, our approach is realized through considering a set of the fitness functions instead of one and only one fitness function, which makes the ensemble selecting of attribute is possible. The experimental results on 10 UCI data sets and 2 KEEL data sets demonstrate that our ensemble selector is effective in improving the stabilities of both reducts and classification results. In addition, the classification accuracies can also be increased.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, C.Z., Shao, M.W., He, Q., Qian, Y.H., Qi, Y.L.: Feature subset selection based on fuzzy neighborhood rough sets. Knowl. Based Sys. 111, 173–179 (2016)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gener. Syst. 17, 191–209 (1990)
Dou, H.L., Yang, X.B., Song, X.N., Yu, H.L., Wu, W.Z.: Decision-theoretic rough set: a multicost strategy. Knowl. Based Syst. 91, 71–83 (2016)
Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. J. Artif. Intell. Res. 6, 1–34 (1997)
Tsang, E.C.C., Hu, Q.H., Chen, D.G.: Feature and instance reduction for PNN classifiers based on fuzzy rough sets. Int. J. Mach. Learn. Cybern. 7, 1–11 (2016)
Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reduction based on variable precision rough set model. Inf. Sci. 159, 255–272 (2004)
Li, J.Y., Fong, S., Wong, R.K., Millham, R., Wong, K.K.L.: Elitist binary wolf search algorithm for heuristic feature selection in high-dimensional bioinformatics datasets. Sci. Rep. 254, 19–28 (2017)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51, 181–207 (2003)
Hu, Q.H., Yu, D.R., Xie, Z.X.: Neighborhood classifier. Expert Syst. Appl. 34, 866–876 (2008)
Hu, Q.H., Liu, J.F., Wu, C.X.: Neighborhood rough set based heterogeneous feature subset selection. Inf. Sci. 18, 3577–3594 (2008)
Hu, Q.H., Pedrycz, W., Yu, D.R., Liang, J.: Selecting discrete and continuous features based on neighborhood decision error minimization. IEEE Trans. Syst. Man Cybern. Part B 40, 137–150 (2010)
Hu, Q.H., Yu, D.R., Xie, Z.X., Li, X.D., Ensemble Rough Subspaces: EROS. Pattern Recogn. 40, 3728–3739 (2007)
Hu, Q.H., An, S., Yu, X., Yu, D.R.: Robust fuzzy rough classifiers. Fuzzy Sets Syst. 183, 26–43 (2011)
Hu, Q.H., Zhang, L., An, S., Zhang, D., Yu, D.R.: On robust fuzzy rough set models. IEEE Trans. Fuzzy Syst. 20, 636–651 (2012)
Xu, S.P., Yang, X.B., Yu, H.L., Tsang, E.C.C.: Multi-label learning with label-specific feature reduction. Knowl. Based Syst. 104, 52–61 (2016)
Xu, S.P., Yang, X.B., Song, X.N., Yu, H.L.: Prediction of protein structural classes by decreasing nearest neighbor error rate. In: 2015 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 12–15 July 2015, pp. 7–13 (2015)
Xu, S., Wang, P., Li, J., Yang, X., Chen, X.: Attribute reduction: an ensemble strategy. In: Polkowski, L. (ed.) IJCRS 2017. LNCS, vol. 10313, pp. 362–375. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60837-2_30
Li, S.Q., Harner, E.J., Adjeroh, D.A.: Random KNN feature selection-a fast and stable alternative to random forests. BMC Bioinform. 12, 1–11 (2011)
Zhao, S.Y., Chen, H., Li, C.P., Du, X.Y., Sun, H.: A novel approach to building a robust fuzzy rough classifier. IEEE Trans. Fuzzy Syst. 23, 769–786 (2015)
Yang, X.B., Qi, Y., Yu, H.L., Song, X.N., Yang, J.Y.: Updating multigranulation rough approximations with increasing of granular structures. Knowl. Based Syst. 64, 59–69 (2014)
Yang, X.B., Xu, S.P., Dou, H.L., Song, X.N., Yu, H.L., Yang, J.Y.: Multigranulation rough set: a multiset based strategy. Int. J. Comput. Intell. Syst. 10, 277–292 (2017)
Zhang, X., Mei, C.L., Chen, D.G., Li, J.H.: Feature selection in mixed data: a method using a novel fuzzy rough set-based information entropy. Pattern Recogn. 56, 1–15 (2016)
Wang, X.Z., Xing, H.J., Li, Y., Hua, Q., Dong, C.R., Pedrycz, W.: A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans. Fuzzy Syst. 23(5), 1638–1654 (2014)
Yao, Y.Y., Zhao, Y., Wang, J.: On reduct construction algorithms. Trans. Comput. Sci. 2, 100–117 (2008)
Qian, Y.H., Wang, Q., Cheng, H.H., Liang, J.Y., Dang, C.Y.: Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst. 258, 61–78 (2015)
Zhou, Z.H., Yu, Y.: Ensembling local learners through multimodal perturbation. IEEE Trans. Syst. Man Cybern. Part B 35(4), 725–735 (2005)
Acknowledgments
This work is supported by the Natural Science Foundation of China (Nos. 61572242, 61502211, 61503160).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wen, H., Eric, A., Chen, X., Liu, K., Wang, P. (2018). NDER Attribute Reduction via an Ensemble Approach. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-99368-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99367-6
Online ISBN: 978-3-319-99368-3
eBook Packages: Computer ScienceComputer Science (R0)