Open AccessResearch of interval reduction in variable precision rough set
Author(s) -
Chen Shenghai,
Jiang Fengying,
Mi Xianwu
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1194
Subject(s) - rough set , reduction (mathematics) , interval (graph theory) , mathematics , hierarchy , heuristic , property (philosophy) , core (optical fiber) , algorithm , set (abstract data type) , variable (mathematics) , matrix (chemical analysis) , data mining , computer science , mathematical optimization , combinatorics , telecommunications , philosophy , mathematical analysis , materials science , geometry , epistemology , economics , market economy , composite material , programming language
The essence of reduction is to find a minimal attribute subset having the same classification ability without inducing new inconsistency with a given decision information system. In variable precision rough set, interval reduction based on β‐classification quality leads to several kinds of reduction anomalies. In this study, the authors define interval reduction based on β‐lower approximation distribution to avoid all kinds of reduction anomalies. The merger of condition classes is discussed and a method is presented to get interval reduction based on the ordered discernibility matrix of condition classes. The property of interval core attribute which is the most important attribute on the given β ‐interval is also discussed and a method is given to calculate core attribute on β‐lower approximation distribution hierarchy. In addition, the core attribute set is usually used as the original subset in the heuristic algorithm for attribute reduction.