Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Chapter

Download BibTeX

Title

Interpretation of Variable Consistency Dominance-Based Rough Set Approach by Minimization of Asymmetric Loss Function

Authors

| [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2019

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • rough sets
  • variable consistency dominance-based rough set approach
  • ordinal data analysis
  • empirical risk minimization
Abstract

EN In this paper, we give a statistical interpretation of the variable consistency dominance-based rough set approach (VC-DRSA), which is a version of DRSA useful for practical reasoning about ordinal data. This study is building a bridge between theories of rough sets and statistics, and it is developing, moreover, a new direction of study about VC-DRSA. We consider a classification problem for each pair of complementary upward and downward unions of decision classes, and define an empirical risk function with an asymmetric loss function consisting of hinge and 0–1 loss functions. Then, we prove that approximations of two decision classes by VC-DRSA correspond to the minimum of the empirical risk function.

Date of online publication

07.03.2019

Pages (from - to)

135 - 145

DOI

10.1007/978-3-030-14815-7_12

URL

https://link.springer.com/chapter/10.1007/978-3-030-14815-7_12

Book

Integrated Uncertainty in Knowledge Modelling and Decision Making : 7th International Symposium, IUKM 2019Nara, Japan, March 27–29, 2019 : Proceedings

Presented on

International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making IUKM 2019, 27-29.03.2019, Nara, Japan

Ministry points / chapter

20

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.