Uncertainty Measure Based on Rough Set in Information Systems
Pages 68 - 71
Abstract
Abstract. Since Shannon put forward information entropy and used it to measure the amount of information in the information system, people began to explore various new methods to measure the uncertainty in the information system. Rough set is a method to solve uncertainty problems, and the measurement of knowledge uncertainty is an important content in the research of rough set theory. Many scholars have explored this from different perspectives. This paper analyzes the uncertainty of knowledge from a new perspective based on Pawlak's definition of the degree of knowledge uncertainty contained in approximate sets, and provides a new measure of knowledge uncertainty. Compared to existing similar measures, this measure not only better reflects the connotation of knowledge uncertainty in the approximation space described by Pawlak, but also is computationally feasible. The research helps people better understand the causes of uncertainty in approximation spaces, and expands and enhances the applicability of rough set theory.
References
[1]
Bin, Y., Ruihui, X., Yu, F. & Zeshui, X. A Novel Dominance-Based Rough Set Model with Advantage (Disadvantage) Neighborhoods and its Applications to Assess Sales Group. 24, 3501-3512 (2023).
[2]
Asma, T., Zied, E. & Eric, L. An Ensemble Classifier through Rough Set Reducts for Handling Data with Evidential Attributes. Inform. Sciences. 635, 414-429 (2023).
[3]
Jaroslaw, S. & Andrzej, S. Three-Way Approximation of Decision Granules Based On the Rough Set Approach. Int. J. Approx. Reason. 155, 1-16 (2023).
[4]
Baoli, W., Jiye, L. & Yiyu, Y. A Trilevel Analysis of Uncertainty Measuresin Partition-Based Granular Computing. Artif. Intell. Rev. 56, 533-575 (2022).
[5]
Barman, B. & Patra, S. Variable Precision Rough Set Based Unsupervised Band Selection Technique for Hyperspectral Image Classification. Knowl.-Based Syst. 193, 1-10 (2020).
[6]
Pawlak, Z. Rough Sets. International Journal of Computer and Information Sciences. 11, 341-356 (1982).
[7]
Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, 1991).
[8]
Yan, Y., Wu, J., Wu, S. & Zhang, J. Research On Condition Monitoring of Power Big Data Based On Rough Sets. International Conference on Materials Engineering and Information Technology Applications (MEITA). Guilin, PEOPLES R CHINA, 2015:760-765.
[9]
Li, F., Yang, J., Jin, C. & Guo, C. A New Effect-Based Roughness Measure for Attribute Reduction in Information System. Inform. Sciences. 378, 348-362 (2017).
[10]
Qian, Y., Liang, X. & Wang, Q. Local Rough Set: A Solution to Rough Data Analysis in Big Data. Int. J. Approx. Reason. 97, 38-63 (2018).
[11]
Miao, D. & Wang, J. On the Relationships Between Infromation Entropy and Roughness of Knowledge in Rough Sets Theory. Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence. 11, 34-40 (1998).
[12]
Düntsch, I. & Gediga, G. Uncertainty Measures of Rough Set Prediction. Artif. Intell. 106, 109-137 (1998).
[13]
Liang, J. Y. & Qian, Y. H. Information Granules and Entropy Theory in Information Systems. Science in China, SeriesE: Information Sciences. 38, 2048-2065 (2008).
[14]
Shannon, C. E. A Mathematical Theory of Communication. The Bell SYstem Technical Journal. 3-4, 373-423, 623-656 (1948).
[15]
Yao, Y. Y. Probabilistic Approaches to Rough Sets. Expert Syst. 20, 287-297 (2003).
[16]
Liang, J. Y. & Shi, Z. Z. The Information Entropy, Rough Entropy and Knowledge Granulation in Rough Set Theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 12, 37-46 (2004).
[17]
Liang, J. Y., Dang, C. Y. & Chin, K. S. A New Method for Measuring Uncertainty and Fuzziness in Rough Set Theory. Int. J. Gen. Syst. 31, 331-342 (2002).
[18]
Qian, Y. H. & Liang, J. Y. Combination Entropy and Combination Granulation in Rough Set Theory. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems. 16, 179-193 (2008).
[19]
Zhu, P. & Wen, Q. Y. Information-Theoretic Measures Associated with Rough Set Approximations. Inform. Sciences. 212, 33-43 (2012).
Index Terms
- Uncertainty Measure Based on Rough Set in Information Systems
Recommendations
A binary granule representation for uncertainty measures in rough set theory
Uncertainty measures can supply new points of view for analyzing data and help us to disclose the substantive characteristics of data sets. Accuracy and roughness proposed by Pawlak are mainly two important measures to deal with uncertainty information ...
The uncertainty of probabilistic rough sets in multi-granulation spaces
Pawlak's rough sets model describes an uncertain target set (concept) with two crisp boundary lines (i.e. lower and upper approximation sets) and as an effective tool has successfully been used to deal with uncertain information systems. Based on the ...
Monotonic uncertainty measures for attribute reduction in probabilistic rough set model
Attribute reduction is one of the most fundamental and important topics in rough set theory. Uncertainty measures play an important role in attribute reduction. In the classical rough set model, uncertainty measures have the monotonicity with respect to ...
Comments
Information & Contributors
Information
Published In
June 2023
124 pages
ISBN:9798400700583
DOI:10.1145/3606843
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 August 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ITCC 2023
ITCC 2023: 2023 5th International Conference on Information Technology and Computer Communications
June 15 - 17, 2023
Tianjin, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 16Total Downloads
- Downloads (Last 12 months)12
- Downloads (Last 6 weeks)0
Reflects downloads up to 24 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format