An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

Details

Serval ID
serval:BIB_7163A1EFBFBB
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System
Journal
IEEE Transactions on Fuzzy Systems
Author(s)
Hosseini R., Qanadli S., Barman S., Mazinani M., Ellis T., Dehmeshki J.
ISSN
1063-6706
Publication state
Published
Issued date
2011
Peer-reviewed
Oui
Pages
-
Language
english
Notes
ahaedofpublication
Abstract
The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.
Web of science
Create date
06/03/2012 8:44
Last modification date
20/08/2019 14:29
Usage data