A hybrid neural network system for pattern classification tasks with missing features

Author

Lim, Chee-Peng ; Leong, Jenn-Hwai ; Kuan, Mei-Ming

Author_Institution

Sch. of Electr. & Electron. Eng., Sci. Malaysia Univ., Penang, Malaysia

Volume

27

Issue

4

fYear

2005

fDate

4/1/2005 12:00:00 AM

Firstpage

648

Lastpage

653

Abstract

A hybrid neural network comprising fuzzy ARTMAP and fuzzy c-means clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification tasks are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.

Keywords

ART neural nets; fuzzy set theory; learning (artificial intelligence); pattern classification; fuzzy c-means clustering; hybrid neural network system; pattern classification task; Backpropagation; Benchmark testing; Biomedical imaging; Fuzzy neural networks; Learning systems; Machine vision; Neural networks; Pattern classification; Subspace constraints; System testing; Fuzzy ARTMAP; Fuzzy c-Means Clustering; Index Terms- Missing data; pattern classification.; Acute Disease; Algorithms; Coronary Disease; Decision Support Systems, Clinical; Diabetes Mellitus; Diagnosis, Computer-Assisted; Fuzzy Logic; Humans; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Syndrome;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2005.64

Filename

1401918