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

Part of the Smart Innovation, Systems and Technologies book series (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 RoughNon − deterministicInformationAnalysis (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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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Basic Sciences, Faculty of EngineeringKyushu Institute of TechnologyTobataJapan
  2. 2.Faculty of Education and Welfare ScienceOita UniversityDannoharuJapan
  3. 3.Faculty of Management and Information ScienceJosai International UniversityToganeJapan

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