Rough Support Vector Machine for Classification with Interval and Incomplete Data

Robert K. Nowicki; Konrad Grzanek; Yoichi Hayashi

doi:10.2478/jaiscr-2020-0004

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.

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Rough Support Vector Machine for Classification with Interval and Incomplete Data

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