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Advanced Intelligent Systems pp 25–33Cite as

Robust Gaussian Kernel Based Approach for Feature Selection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 268))

Abstract

The outlier problem of feature selection is rarely discussed in the most previous works. Moreover, there are no work has been reported in literature on symbolic interval feature selection in the supervised framework. In this paper, we will incorporate similarity margin concept and Gaussian kernel fuzzy rough sets to deal with the Symbolic Data Selection problem and it is also an optimizing problem. The advantage of this approach is it can easily introduce loss function and with robustness.

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References

  1. Diday, E., Esposito, F.: An Introduction to Symbolic Data Analysis and the SODAS software. Intelligent Data Analysis 7, 583–601 (2003)

    Google Scholar 

  2. Gowda, K.C., Diday, E.: Symbolic clustering using a new similarity measure. IEEE Trans. Systems Man Cyber. 22, 368–378 (1992)

    Article  Google Scholar 

  3. Guoa, J., Li, W., Li, C., Gaoa, S.: Standardization of interval symbolic data based on the empirical descriptive statistics. Computational Statistics and Data Analysis 56, 602–610 (2012)

    Article  MathSciNet  Google Scholar 

  4. Dash, M., Liu, H.: Consistency-based search in feature selection. Artificial Intelligence 151, 155–176 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  5. Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research 5, 1205–1224 (2004)

    MATH  Google Scholar 

  6. Zhu, Z.X., Ong, Y.S., Dash, M.: Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans. on SMC Part B 37, 70–76 (2007)

    Google Scholar 

  7. Wanga, F., Lianga, J., Dangb, C.: Attribute reduction for dynamic data sets. Applied Soft Computing 13, 676–689 (2013)

    Article  Google Scholar 

  8. Hu, Q.H., Xie, Z.X., Yu, D.R.: Hybrid attribute reduction based on a novel fuzzy- rough model and information granulation. Pattern Recognition 40, 3509–3521 (2007)

    Article  MATH  Google Scholar 

  9. Pawlak, Z.: Rough sets. Int. J. Coinput. Inf. Sci. 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  10. 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. Information Sciences 176, 1829–1848 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  11. 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 Transactions on Fuzzy Systems 13, 343–361 (2005)

    Article  Google Scholar 

  12. Jensen, R., Shen, Q.: Fuzzy-rough attributes reduction with application to web categoriza-tion. Fuzzy Sets and Systems 141, 469–485 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  13. Tsang, E.C.C., Chen, D.G., Yeung, D.S., Wang, X.Z., Lee, J.W.T.: Attributes reduction us-ing fuzzy rough sets. IEEE Transaction on Fuzzy System 16(5), 1130–1141 (2008)

    Article  Google Scholar 

  14. Jensen, R., Shen, Q.: New Approaches to Fuzzy-Rough Feature Selection. IEEE Transaction on Fuzzy System 17(4), 814–838 (2009)

    Article  Google Scholar 

  15. Wu, H., Wu, Y., Luo, J.: An Interval Type-2 Fuzzy Rough Set Model for Attribute Reduction. IEEE Trans. Fuzz. Sys. 17(2) (2009)

    Google Scholar 

  16. Hua, Q., Zhang, L., Chen, D., Pedrycz, W., Yu, D.: Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications. International Journal of Approximate Reasoning 51, 453–471 (2010)

    Article  Google Scholar 

  17. Chen, D., Hu, Q., Yang, Y.: Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets. Information Sciences 181, 5169–5179 (2011)

    Article  MATH  Google Scholar 

  18. Ma, T.-H., Tang, M.-L.: Weighted rough set model. In: Int. Conf. Intelligent Systems Design and Applications, pp. 481–485 (2006)

    Google Scholar 

  19. Liu, J., Hu, Q., Yu, D.: A weighted rough set based method developed for class imbalance learning. Information Sciences 178, 1235–1256 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  20. Liang, J.Y., Chin, K.S., Dang, C.Y., Yam Richid, C.M.: A new method for measuring un-certainty and fuzziness in rough set theory. International Journal of General Systems 31(4), 331–342 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  21. Hu, Q.H., Xie, Z.X., Yu, D.R.: Hybrid attribute reduction based on a novel fuzzy- rough model and information granulation. Pattern Recognition 40, 3509–3521 (2007)

    Article  MATH  Google Scholar 

  22. Gorzalczany, B.: Interval-valued fuzzy controller based on verbal modal of object. Fuzzy Sets and Systems 28, 45–53 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  23. Gong, Z.T., Sun, B.Z., Chen, D.G.: Rough set theory for the interval-valued fuzzy in-formation systems. Information Sciences 178, 1968–1985 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  24. Hedjazi, L., Aguilar-Martin, J., Le Lann, M.-V.: Similarity-margin based feature selection for symbolic interval data. Pattern Recognition Letters 32, 578–585 (2011)

    Article  Google Scholar 

  25. Quevedo, J., Puig, V., Cembrano, G., Blanch, J., Aguilar, J., Saporta, D., Benito, G., He-do, M., Molina, A.: Validation and reconstruction of flow meter data in the Barcelona water distribution network. J. Control Eng. Practice 18, 640–651 (2010)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, Kao Yuan University, Kaohsiung, Taiwan

    Chih-Ching Hsiao

  2. Department of Electrical Engineering, National Ilan University, Yilan, Taiwan

    Chen-Chia Chuang

  3. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shun-Feng Su

Authors
  1. Chih-Ching Hsiao
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  2. Chen-Chia Chuang
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  3. Shun-Feng Su
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Corresponding author

Correspondence to Chih-Ching Hsiao .

Editor information

Editors and Affiliations

  1. Daejeon University, Daejeon, Korea, Republic of (South Korea)

    Yong Soo Kim

  2. Mokpo National University, Jeonnam, Korea, Republic of (South Korea)

    Young J. Ryoo

  3. Seokyung University, Seoul, Korea, Republic of (South Korea)

    Moon-soo Jang

  4. Chonnam National University, Gwangju, Korea, Republic of (South Korea)

    Young-Chul Bae

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© 2014 Springer International Publishing Switzerland

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Hsiao, CC., Chuang, CC., Su, SF. (2014). Robust Gaussian Kernel Based Approach for Feature Selection. In: Kim, Y., Ryoo, Y., Jang, Ms., Bae, YC. (eds) Advanced Intelligent Systems. Advances in Intelligent Systems and Computing, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-319-05500-8_4

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  • DOI: https://doi.org/10.1007/978-3-319-05500-8_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05499-5

  • Online ISBN: 978-3-319-05500-8

  • eBook Packages: EngineeringEngineering (R0)

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