Similarity relations and fuzzy orderings
Information Sciences: an International Journal
Principal component analysis of fuzzy data using autoassociative neural networks
IEEE Transactions on Fuzzy Systems
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
Classification based on fuzzy robust PCA algorithms and similarity classifier
Expert Systems with Applications: An International Journal
Nonlinear fuzzy robust PCA algorithms and similarity classifier in bankruptcy analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Similarity classifier with ordered weighted averaging operators
Expert Systems with Applications: An International Journal
Fuzzy classifier based on fuzzy support vector machine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
| Hi-index | 12.06 |
In this article we propose a method which tackles a problem where data is linguistic instead of real valued numbers. The proposed method starts with representing data as fuzzy numbers. Then generalized principle component analysis (PCA) is used, which can be used to reduce the data dimensionality and also to clear out some irregularitises from the data. After this, the data is defuzzified and then the similarity classifier is used to get the required classification accuracy. Here post-operative patient data set is used to build this expert system to determine based on hypothermia condition, whether patients in a post-operative recovery area should be sent to Intensive Care Unit, general hospital floor or go home. What makes this task particularly difficult is that most of the measured attributes have linguistic values (e.g. stable, moderately stable, unstable, etc.). Results are compared to existing result in literature and this system provides mean classification accuracy of 62.7% where as second highest reported results are with linguistic hard C-mean with 53.3%.