Paper Info
Title
A unified incremental reduction with the variations of the object for decision tables
Abstract
Attribute reduction is one of the issues in the rough set theory, and many reduction algorithms have been proposed to process the static decision systems. However, in real-life applications, the object may vary dynamically with time. In order to deal with common variations of the object (i.e., adding the object, deleting the object and modifying the object’s value) in the decision systems, the paper presents a unified incremental reduction algorithm for three types of the dynamic object from the viewpoint of the discernibility matrix. Firstly, the concepts of the equivalence class linked table (ECLT) and the simplified decision table are proposed. On the basis of ECLT, the incremental mechanisms are analyzed and the corresponding functions for updating the discernibility matrix are designed for three variations of the object. And then, the dynamic criterions how to add new attributes into the original reduct or delete redundant attributes from the original reduct are studied. Subsequently, a unified incremental reduction algorithm with varying the object is presented. A serial of experiments are validated to explain the feasibility and effectiveness of the proposed algorithms on different data sets from UCI.
Year
DOI
Venue
2019
10.1007/s00500-018-3296-5
soft computing
Keywords
Field
DocType
Rough set, Positive region, Discernibility matrix, Attribute reduction, Updating algorithm
Data set,Reduct,Decision table,Matrix (mathematics),Computer science,Decision system,Rough set,Theoretical computer science,Equivalence class
Journal
Volume
Issue
ISSN
23.0
15.0
1433-7479
Citations 
PageRank 
References 
0
0.34
32
Authors
4
Name
Order
Citations
PageRank
Chuanjian Yang1123.02
Hao Ge2132.51
Li Longshu300.68
J. Ding4105.11