Abstract
A hybrid information system with images (HISI) is an information system (IS) where there exist many kinds of data or attributes (e.g., boolean, categorical, real-valued, set-valued, interval-valued, imaged, decision and missing data or attributes). Uncertainty measurement (UM) is an effective tool for evaluation. This article inquires into UM for a HISI with application to attribute reduction. The distance between attribute values in a HISI is first constructed. Then, the tolerance relations on an object set of a HISI are obtained by means of this distance. Next, information structures in a HISI are proposed. After that, UM for a HISI is considered by using its information structures. Moreover, effectiveness analysis for the proposed measures is conducted from the angle of statistics. Finally, an application of the proposed measures in attribute reduction for a HISI is given, and the corresponding algorithms are put forward.
Highlights
INFORMATION STRUCTURES IN A hybrid information system with images (HISI) we propose some concepts of information structures in a HISI
In order to show the performance of the proposed measures for uncertainty in a HISI, we give two numerical experiments continued from Example II.3
In this article, the tolerance relation has been constructed based on the distance between attribute values in a HISI, and the information structures has been introduced
Summary
To evaluate uncertainty of a system, entropy was proposed by Shannon [29] It has been applied in various fields as a very useful method for characterizing information contents in different modes. Beaubouef et al [1] explored a method measuring uncertainty of rough sets and rough relation databases based on rough entropy. Qian and Liang [27] proposed combination entropy with intuitionistic knowledge-content characteristic in an incomplete information system (IIS), which can be used to measure the uncertainty of an IIS. Zeng et al [41] studied a hybrid information system with images (HISI) and gave a fuzzy rough set approach for incremental feature selection on this system.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
