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International Conference on Advanced Machine Learning Technologies and Applications

AMLTA 2012: Advanced Machine Learning Technologies and Applications pp 3–12Cite as

Rough Sets-Based Machine Learning over Non-deterministic Data: A Brief Survey

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 322))

Abstract

Rough Non-deterministic Information Analysis (RNIA) is a rough sets-based framework for handling tables with exact and inexact data. Under this framework, we investigated possible equivalence relations, data dependencies, rule generation, rule stability, question-answering systems, as well as missing and interval values as special cases of non-deterministic values. In this paper, we briefly survey RNIA, and report the state of its underlying software implementation. We also discuss to what extent RNIA can be seen as an example of a new emerging paradigm in machine learning.

This work is supported by the Grant-in-Aid for Scientific Research (C) (No.22500204), Japan Society for the Promotion of Science. The fourth author was partially supported by the Polish National Science Centre grant 2011/01/B/ST6/03867.

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References

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, 804, Japan

    Hiroshi Sakai & Mao Wu

  2. Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba, 283, Japan

    Michinori Nakata

  3. Institute of Mathematics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Dominik Ślęzak

  4. Infobright Inc., Krzywickiego 34 pok. 219, 02-078, Warsaw, Poland

    Dominik Ślęzak

Authors
  1. Hiroshi Sakai
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  2. Mao Wu
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  3. Michinori Nakata
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  4. Dominik Ślęzak
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Editor information

Editors and Affiliations

  1. Cairo University, Egypt

    Aboul Ella Hassanien  & Rabie Ramadan  & 

  2. Ain Shams University, Cairo, Egypt

    Abdel-Badeeh M. Salem

  3. University of Tasmania, TAS, Australia

    Tai-hoon Kim

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© 2012 Springer-Verlag Berlin Heidelberg

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Sakai, H., Wu, M., Nakata, M., Ślęzak, D. (2012). Rough Sets-Based Machine Learning over Non-deterministic Data: A Brief Survey. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_1

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  • DOI: https://doi.org/10.1007/978-3-642-35326-0_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35325-3

  • Online ISBN: 978-3-642-35326-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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