TY - GEN
T1 - A hybrid RBF-ART model and its application to medical data classification
AU - Tan, Shing Chiang
AU - Lim, Chee Peng
AU - Watada, Junzo
PY - 2013
Y1 - 2013
N2 - In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment algorithm (i.e., RBFNDDA) to undertake data classification problems is proposed. The new network is formed by integrating the learning algorithm of the Fuzzy ARTMAP (FAM) neural network into RBFNDDA. The proposed RBFNDDA-FAM network inherits the salient features of FAM and overcomes the shortcomings of the original RBFNDDA network. The effectiveness of RBFNDDA-FAM is demonstrated using two benchmark problems. The first involves an artificial data set whereas the second uses a medical data set related to thyroid diagnosis. The results from these studies are compared, analyzed, and discussed. The outcomes positively reveal the potentials of RBFNDDA-FAM in learning information with a compact network architecture, in addition to high classification performances.
AB - In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment algorithm (i.e., RBFNDDA) to undertake data classification problems is proposed. The new network is formed by integrating the learning algorithm of the Fuzzy ARTMAP (FAM) neural network into RBFNDDA. The proposed RBFNDDA-FAM network inherits the salient features of FAM and overcomes the shortcomings of the original RBFNDDA network. The effectiveness of RBFNDDA-FAM is demonstrated using two benchmark problems. The first involves an artificial data set whereas the second uses a medical data set related to thyroid diagnosis. The results from these studies are compared, analyzed, and discussed. The outcomes positively reveal the potentials of RBFNDDA-FAM in learning information with a compact network architecture, in addition to high classification performances.
KW - Adaptive resonance theory neural network
KW - Classification
KW - Clinical decision support
KW - Hybrid learning
KW - Radial basis function neural network
UR - http://www.scopus.com/inward/record.url?scp=84896889257&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896889257&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-264-6-21
DO - 10.3233/978-1-61499-264-6-21
M3 - Conference contribution
AN - SCOPUS:84896889257
SN - 9781614992639
VL - 255
T3 - Frontiers in Artificial Intelligence and Applications
SP - 21
EP - 30
BT - Frontiers in Artificial Intelligence and Applications
ER -