Paper Info
Title
A Real-Time Analysis Of Granular Information: Some Initial Thoughts On A Convex Hull-Based Fuzzy Regression Approach
Abstract
Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-Fuzzy C-Means (GA-FCM) and a convex hull-based fuzzy regression approach being regarded as a potential solution to the formation of information granules. It is anticipated that the setting of Granular Computing will help us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time granular fuzzy regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. In the proposed design setting, we emphasize a pivotal role of the convex hull approach, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.
Year
DOI
Venue
2011
10.1109/FUZZY.2011.6007429
IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
Keywords
Field
DocType
convex hull, fuzzy regression, Fuzzy C-Means, genetic algorithm, granular computing
Mathematical optimization,Algorithm design,Computer science,Convex hull,Fuzzy set,Granular computing,Linear programming,Artificial intelligence,Cluster analysis,Output-sensitive algorithm,Genetic algorithm,Machine learning
Conference
ISSN
Citations 
PageRank 
1098-7584
0
0.34
References 
Authors
17
4
Name
Order
Citations
PageRank
Azizul Azhar Ramli1165.25
W. Pedrycz2139661005.85
Junzo Watada341184.53
Nureize Arbaiy4259.78