Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 11304))
Included in the following conference series:
2263Accesses
Abstract
Previous studies have shown that information entropy and its variants are useful at reducing data dimensionality. Yet, most existing approaches based on entropy exploit the correlations between features and labels, lacking of taking into account the relevance between features. In this paper, we propose a new index for feature selection, named fuzzy conditional distinction degree (FDD), based on fuzzy similarity relation by combining feature correlations with the relationship between features and labels. Different from existing approaches based on entropy, FDD considers the cardinality of the relation matrix instead of the similarity classes. Meanwhile, we encode the feature correlations into distance to measure the relevance of any two features. Some useful properties are discussed. Based on the FDD, a greedy forward algorithm for feature selection is presented. Experimental results on benchmark data sets denote the feasibility and effectiveness of the proposed approach.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 5719
- Price includes VAT (Japan)
- Softcover Book
- JPY 7149
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw.5(4), 537–550 (1994)
Dai, J., Wang, W., Xu, Q.: An uncertainty measure for incomplete decision tables and its applications. IEEE Trans. Cybern.43(4), 1277–1289 (2013)
Dai, J., Xu, Q.: Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl. Soft Comput. J.13(1), 211–221 (2013)
Dai, J., Xu, Q., Wang, W., Tian, H.: Conditional entropy for incomplete decision systems and its application in data mining. Int. J. Gen. Syst.41(7), 713–728 (2012)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res.7, 1–30 (2006)
Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), pp. 359–366 (2000)
Hu, Q., Yu, D., Xie, Z., Liu, J.: Fuzzy probabilistic approximation spaces and their information measures. IEEE Trans. Fuzzy Syst.14(2), 191–201 (2006)
Hu, Q., Zhang, L., Zhang, D., Pan, W., An, S., Pedrycz, W.: Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Syst. Appl.38(9), 10737–10750 (2011)
Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst.17(4), 824–838 (2009)
Tallón-Ballesteros, A.J., Riquelme, J.C.: Tackling ant colony optimization meta-heuristic as search method in feature subset selection based on correlation or consistency measures. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds.) IDEAL 2014. LNCS, vol. 8669, pp. 386–393. Springer, Cham (2014).https://doi.org/10.1007/978-3-319-10840-7_47
Tang, J., Liu, H.: Unsupervised feature selection for linked social media data. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 904–912 (2012)
Tiwari, A.K., Shreevastava, S., Som, T., Shukla, K.K.: Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction. Expert Syst. Appl.101, 205–212 (2018)
Wang, C., Hu, Q., Wang, X., Chen, D., Qian, Y., Dong, Z.: Feature selection based on neighborhood discrimination index. IEEE Trans. Neural Netw. Learn. Syst.29(7), 2986–2999 (2017)
Wang, C., Qi, Y., Shao, M., Hu, Q., Chen, D., Qian, Y., Lin, Y.: A fitting model for feature selection with fuzzy rough sets. IEEE Trans. Fuzzy Syst.25(4), 741–753 (2017)
Wang, C., Shao, M., He, Q., Qian, Y., Qi, Y.: Feature subset selection based on fuzzy neighborhood rough sets. Knowl.-Based Syst.111, 173–179 (2016)
Witten, I.H., Eibe, F., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Elsevier, Burlington (2011)
Yager, R.R.: Entropy measures under similarity relations. Int. J. Gen. Syst.20(4), 341–358 (1992)
Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21–24, 2003, Washington, DC, pp. 856–863 (2003)
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (Nos. 61473259, 61502335, 61070074, 60703038) and the Hunan Provincial Science and Technology Project Foundation (2018TP1018, 2018RS3065).
Author information
Authors and Affiliations
School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China
Qilai Zhang
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, China
Jianhua Dai
- Qilai Zhang
You can also search for this author inPubMed Google Scholar
- Jianhua Dai
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toJianhua Dai.
Editor information
Editors and Affiliations
The Chinese Academy of Sciences, Beijing, China
Long Cheng
City University of Hong Kong, Kowloon, Hong Kong
Andrew Chi Sing Leung
Kobe University, Kobe, Japan
Seiichi Ozawa
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Q., Dai, J. (2018). Feature Selection Based on Fuzzy Conditional Distinction Degree. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_7
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-030-04211-0
Online ISBN:978-3-030-04212-7
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative