Rough Support Vector Machine for Classification with Interval and Incomplete Data

Robert K. Nowicki, Konrad Grzanek, Yoichi Hayashi

Research output: Contribution to journalArticle

Abstract

The paper presents the idea of connecting the concepts of the Vapnik's support vector machine with Pawlak's rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three-way decision. The proposed solution will be tested using several popular benchmarks.

Original languageEnglish
Pages (from-to)47-56
Number of pages10
JournalJournal of Artificial Intelligence and Soft Computing Research
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Fingerprint

Interval Data
Incomplete Data
Rough Set
Rough
Support vector machines
Support Vector Machine
Equivalence classes
Missing Values
Equivalence class
Hybrid systems
Hybrid Systems
Benchmark
Interval
Form
Class
Concepts

Keywords

  • interval data
  • missing features
  • rough sets
  • support vector machines
  • three-way decision

Cite this

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Rough Support Vector Machine for Classification with Interval and Incomplete Data. / Nowicki, Robert K.; Grzanek, Konrad; Hayashi, Yoichi.

In: Journal of Artificial Intelligence and Soft Computing Research, Vol. 10, No. 1, 01.01.2020, p. 47-56.

Research output: Contribution to journalArticle

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