TY - JOUR
T1 - A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge
AU - Jiao, Lianmeng
AU - Denoeux, Thierry
AU - Pan, Quan
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2016/12
Y1 - 2016/12
N2 - In some real-world classification applications, such as target recognition, both training data collected by sensors and expert knowledge may be available. These two types of information are usually independent and complementary, and both are useful for classification. In this paper, a hybrid belief rule-based classification system (HBRBCS) is developed to make joint use of these two types of information. The belief rule structure, which is capable of capturing fuzzy, imprecise, and incomplete causal relationships, is used as the common representation model. With the belief rule structure, a data-driven belief rule base (DBRB) and a knowledge-driven belief rule base (KBRB) are learned from uncertain training data and expert knowledge, respectively. A fusion algorithm is proposed to combine the DBRB and KBRB to obtain an optimal hybrid belief rule base (HBRB). A belief reasoning and decision-making module is then developed to classify a query pattern based on the generated HBRB. An airborne target classification problem in the air surveillance system is studied to demonstrate the performance of the proposed HBRBCS for combining both uncertain sensor measurements and expert knowledge to make classification.
AB - In some real-world classification applications, such as target recognition, both training data collected by sensors and expert knowledge may be available. These two types of information are usually independent and complementary, and both are useful for classification. In this paper, a hybrid belief rule-based classification system (HBRBCS) is developed to make joint use of these two types of information. The belief rule structure, which is capable of capturing fuzzy, imprecise, and incomplete causal relationships, is used as the common representation model. With the belief rule structure, a data-driven belief rule base (DBRB) and a knowledge-driven belief rule base (KBRB) are learned from uncertain training data and expert knowledge, respectively. A fusion algorithm is proposed to combine the DBRB and KBRB to obtain an optimal hybrid belief rule base (HBRB). A belief reasoning and decision-making module is then developed to classify a query pattern based on the generated HBRB. An airborne target classification problem in the air surveillance system is studied to demonstrate the performance of the proposed HBRBCS for combining both uncertain sensor measurements and expert knowledge to make classification.
KW - Belief rule
KW - expert knowledge
KW - hybrid classification system
KW - Uncertain data
UR - http://www.scopus.com/inward/record.url?scp=84999751843&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2015.2503381
DO - 10.1109/TSMC.2015.2503381
M3 - 文章
AN - SCOPUS:84999751843
SN - 2168-2216
VL - 46
SP - 1711
EP - 1723
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 12
M1 - 7365464
ER -