Open AccessStable attribute reduction for neighborhood rough set
Author(s) -
Shaochen Liang,
Xibei Yang,
Xiangjian Chen,
Jingzheng Li
Publication year - 2018
Publication title -
filomat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 34
eISSN - 2406-0933
pISSN - 0354-5180
DOI - 10.2298/fil1805809l
Subject(s) - rough set , reduct , mathematics , reduction (mathematics) , heuristic , stability (learning theory) , set (abstract data type) , data mining , algorithm , mathematical optimization , computer science , machine learning , geometry , programming language
In neighborhood rough set theory, traditional heuristic algorithm for computing reducts does not take the stability of the selected attributes into account, it follows that the performances of the reducts may not be good enough if the perturbations of data occur. To fill the gap, the mechanism of acquiring the most significant attribute is realized by two steps in the reduction process: firstly, several important attributes are derived in each iteration based on several radii which are close to the given radius for computing reduct; secondly, the most significant attribute is selected from them by a voting strategy. The experiments verify that such method can effectively improve the stabilities of the reducts, and it does not require too much attributes for constructing the reducts.