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Rough Non-deterministic Information Analysis: Foundations and Its Perspective in Machine Learning

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Emerging Paradigms in Machine Learning

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 13))

Abstract

This chapter focuses on a mathematical framework for handling information incompleteness, which is deeply related to machine learning. Recently, the handling of the information incompleteness in data sets is recognized to be very important research area for machine learning. We have already proposed a framework Rough Non − deterministic Information Analysis (RNIA). This is a rough sets based framework for handling not only definite (or complete) information but also indefinite (or incomplete) information. This RNIA handles lots of aspects in tables with the information incompleteness, i.e., rough sets based issues, data dependencies, question-answering, rule generation, estimation of actual values, etc. Each aspect is extended from tables with complete information to tables with incomplete information according to the modal concepts. We survey this RNIA, and we describe the perspective of RNIA with respect to machine learning.

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Authors and Affiliations

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

    Hiroshi Sakai

  2. Faculty of Education and Welfare Science, Oita University, Dannoharu, Oita, 870, Japan

    Hitomi Okuma

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

    Michinori Nakata

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  1. Hiroshi Sakai
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  2. Hitomi Okuma
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  3. Michinori Nakata
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Correspondence to Hiroshi Sakai .

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Editors and Affiliations

  1. Deptartment of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipegg, R3B 2E9, Canada

    Sheela Ramanna

  2. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

  3. KES International, Shoreham-by-sea, BN43 9AF, United Kingdom

    Robert J. Howlett

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Sakai, H., Okuma, H., Nakata, M. (2013). Rough Non-deterministic Information Analysis: Foundations and Its Perspective in Machine Learning. In: Ramanna, S., Jain, L., Howlett, R. (eds) Emerging Paradigms in Machine Learning. Smart Innovation, Systems and Technologies, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28699-5_9

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