<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Rough Support Vector Machine for Classification with Interval and Incomplete Data

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

Rough Support Vector Machine for Classification with Interval and Incomplete Data

Online veröffentlicht: 11. Dez. 2019

Seitenbereich: 47 - 56

Eingereicht: 23. Juli 2019

Akzeptiert: 19. Nov. 2019

DOI: https://doi.org/10.2478/jaiscr-2020-0004

Schlüsselwörter
support vector machines, rough sets, missing features, interval data, three–way decision

© 2020 Robert K. Nowicki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Rough Support Vector Machine for Classification with Interval and Incomplete Data

Online veröffentlicht: 11. Dez. 2019

Seitenbereich: 47 - 56

Eingereicht: 23. Juli 2019

Akzeptiert: 19. Nov. 2019

DOI: https://doi.org/10.2478/jaiscr-2020-0004

Schlüsselwörtersupport vector machines, rough sets, missing features, interval data, three–way decision

© 2020 Robert K. Nowicki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
support vector machines, rough sets, missing features, interval data, three–way decision