TY - GEN
T1 - Rule extraction method in incomplete decision table for fault diagnosis based on discernibility matrix primitive
AU - Huang, Wentao
AU - Wang, Weijie
AU - Zhao, Xuezeng
PY - 2008
Y1 - 2008
N2 - Compared with extracting rules from complete data, it is more difficult to obtain rules from incomplete data for fault diagnosis. In this paper, based on the rough set theory, a method is proposed to directly extract optimal generalized decision rules from incomplete a decision table for fault diagnosis (IDTFD). The discernibility matrix primitive is defined and characterized to simplify the computing process. A definition of object-oriented discernibility matrix in IDTFD is also proposed. Using these concepts, an object-oriented discernibility function is constructed. With the basic equivalent forms in proposition logic such as distribution laws, absorption laws, a method is proposed to compute the minimal object-oriented reductions and to extract the optimal generalized decision rules in IDTFD. The proposed method is applied in fault diagnosis of operational states of an electric system. The effectiveness of this method is shown in our experiments.
AB - Compared with extracting rules from complete data, it is more difficult to obtain rules from incomplete data for fault diagnosis. In this paper, based on the rough set theory, a method is proposed to directly extract optimal generalized decision rules from incomplete a decision table for fault diagnosis (IDTFD). The discernibility matrix primitive is defined and characterized to simplify the computing process. A definition of object-oriented discernibility matrix in IDTFD is also proposed. Using these concepts, an object-oriented discernibility function is constructed. With the basic equivalent forms in proposition logic such as distribution laws, absorption laws, a method is proposed to compute the minimal object-oriented reductions and to extract the optimal generalized decision rules in IDTFD. The proposed method is applied in fault diagnosis of operational states of an electric system. The effectiveness of this method is shown in our experiments.
KW - Discernibility matrix primitive
KW - Fault diagnosis
KW - Incomplete decision table
KW - Rule extraction
UR - https://www.scopus.com/pages/publications/44649201249
U2 - 10.1007/978-3-540-79721-0_100
DO - 10.1007/978-3-540-79721-0_100
M3 - 会议稿件
AN - SCOPUS:44649201249
SN - 3540797203
SN - 9783540797203
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 755
EP - 762
BT - Rough Sets and Knowledge Technology - Third International Conference, RSKT 2008, Proceedings
T2 - 3rd International Conference on Rough Sets and Knowledge Technology, RSKT 2008
Y2 - 17 May 2008 through 19 May 2008
ER -