Skip to main content
SpringerLink
Log in

Incremental Distributed Weighted Class Discriminant Analysis on Interval-Valued Emitter Parameters

  • Conference paper
  • First Online:
Book cover Knowledge Science, Engineering and Management (KSEM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9403))

  • 2918 Accesses

Abstract

In the age of big data, the emitter parameter measurement data is generally characteristic of uncertainty in the form of normally-distributed intervals, enormous size and continuous growth. However, existing interval-valued data analysis methods generally assume a uniform distribution instead and are unable to adapt to the rapid growth of volume. To address the above problems, we have brought forward an incremental distributed weighted class discriminant analysis method on interval-valued emitter parameters. Extensive experiments indicate that our method is able to cope with these new characteristics effectively.

This work was supported by National Natural Science Foundation of China (No. 61402426, 61373129) and Chemical Sciences, Geosciences and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy (No. DEFG02-91ER20021) and partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mballo, C., Diday, E.: Decision trees on interval valued variables. The Electronic Journal of Symbolic Data Analysis 3(1) (2005)

    Google Scholar 

  2. Caballero, A., Yen, K., Fang, Y., Abreu, J.L.: Method for classification in interval-valued information systems. In: Proceedings of the 12th WSEAS International Conference on Automatic Control, Modelling & Simulation (2010)

    Google Scholar 

  3. Lima Neto, E.A., de Carvalho, F.A.T.: Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500–1515 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Utkin, L., Destercke, S.: Computing expectations with continuous p-boxes: Univariate case. International Journal of Approximate Reasoning 50, 778–798 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  5. De Carvalho, F., De Souza, R., Chavent, M., Lechevallier, Y.: Adaptative Hausdorff Distances and Dynamic Clustering of Symbolic Interval Data. Pattern Recognition Letters 27(3), 167–179 (2006). Elsevier

    Article  Google Scholar 

  6. Noirhomme-Fraiture, M., Brito, P.: Far beyond the classical data models: symbolic data analysis. Statistical Analysis and Data Mining 4(2), 157–170 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Royston, J.P.: Expected Normal Order Statistics (Exact and Approximate). Journal of the Royal Statistical Society. Series C (Applied Statistics) 31(2), 161–165 (1982)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Science and Technology on Information System Engineering Laboratory, NRIEE, Nanjing, China

    Xin Xu

  2. State Key Laboratory for Novel Software and Technology, Nanjing University, Nanjing, China

    Wei Wang

  3. Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University, Beijing, China

    Jiaheng Lu

  4. Department of Energy Plant Research Lab, Michigan State University, East Lansing, USA

    Jin Chen

  5. Department of Computer Science and Engineering, Michigan State University, East Lansing, USA

    Jin Chen

Authors
  1. Xin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiaheng Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jin Chen .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Songmao Zhang

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Martin Wirsing

  3. Southwest University, Chongqing, China

    Zili Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Xu, X., Wang, W., Lu, J., Chen, J. (2015). Incremental Distributed Weighted Class Discriminant Analysis on Interval-Valued Emitter Parameters. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25159-2_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25158-5

  • Online ISBN: 978-3-319-25159-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation