title: A hybrid neural network system for pattern classification tasks with missing features.
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 task are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.
authors.affiliation: School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia. cplim@eng.usm.my
authors.forename: Chee-Peng
authors.initials: CP
authors.lastname: Lim
authors.forename: Jenn-Hwai
authors.initials: JH
authors.lastname: Leong
authors.forename: Mei-Ming
authors.initials: MM
authors.lastname: Kuan
mesh_terms: D000208:Acute Disease; D000465:Algorithms; D003327:Coronary Disease / Q000175:diagnosis; D020000:Decision Support Systems, Clinical; D003920:Diabetes Mellitus / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013577:Syndrome