TY - JOUR
T1 - Evidential inference with embedded pattern classifiers
T2 - Proceedings of the 1996 IEEE International Conference on Systems, Man and Cybernetics. Part 4 (of 4)
AU - Liu, Jiming
AU - Yuen, Pong Chi
AU - Tang, Yuan Y.
AU - Wang, Xiaoru
AU - Ying, Hai
N1 - Copyright:
Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.
PY - 1996
Y1 - 1996
N2 - In this paper, we describe a novel quantitative approach to medical diagnosis. Drawing on sound mathematical theories as well as the promising results of previous experiments, the proposed approach provides a computational solution to the modeling and aggregating of partial evidential observations to assure an accurate diagnosis. This approach is particularly useful for diagnosing cases in which a complete set of symptoms is too difficult to observe and the diagnostic judgments are subject to human errors. This paper presents several experiments in which real-world diagnostic problems were investigated. In particular, it attempts to show that (1) with a limited number of case samples, our implication-induction algorithm is capable of inducing implication networks useful for making evidential inferences based on partial observations, (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events, and (3) the network-based evidentially predicted events or attributes can provide sufficient information for pattern classification.
AB - In this paper, we describe a novel quantitative approach to medical diagnosis. Drawing on sound mathematical theories as well as the promising results of previous experiments, the proposed approach provides a computational solution to the modeling and aggregating of partial evidential observations to assure an accurate diagnosis. This approach is particularly useful for diagnosing cases in which a complete set of symptoms is too difficult to observe and the diagnostic judgments are subject to human errors. This paper presents several experiments in which real-world diagnostic problems were investigated. In particular, it attempts to show that (1) with a limited number of case samples, our implication-induction algorithm is capable of inducing implication networks useful for making evidential inferences based on partial observations, (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events, and (3) the network-based evidentially predicted events or attributes can provide sufficient information for pattern classification.
UR - http://www.scopus.com/inward/record.url?scp=0030405502&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0030405502
SN - 0884-3627
VL - 2
SP - 1096
EP - 1101
JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Y2 - 14 October 1996 through 17 October 1996
ER -