TY - GEN
T1 - Adapting existing BKB structures using new data
AU - Hildeshaim, Tali
AU - Shimony, Solomon Eyal
PY - 2004/12/1
Y1 - 2004/12/1
N2 - Bayesian Knowledge Bases (BKB) are a rule based probabilistic model that extends the well known Bayes Networks (BN), by naturally allowing for context-specific independence and for cycles in the directed graph. The learning process of BKB structures from large datasets consumes enormous amount of computational resources, even when using the somewhat simplified minimum description length (MDL) scoring. When a BKB structure exists for a dataset, adapting the existing structures can be used to expedite the learning process of for datasets that are known to be derived from similar causal structure. Empirical results show that the adaptation method is capable of successfully learning BKB structures that accurately represent the new data, are simple, and retain much of the existing structures.
AB - Bayesian Knowledge Bases (BKB) are a rule based probabilistic model that extends the well known Bayes Networks (BN), by naturally allowing for context-specific independence and for cycles in the directed graph. The learning process of BKB structures from large datasets consumes enormous amount of computational resources, even when using the somewhat simplified minimum description length (MDL) scoring. When a BKB structure exists for a dataset, adapting the existing structures can be used to expedite the learning process of for datasets that are known to be derived from similar causal structure. Empirical results show that the adaptation method is capable of successfully learning BKB structures that accurately represent the new data, are simple, and retain much of the existing structures.
KW - Bayesian Knowledge Bases
KW - Bayesian Networks
KW - Data Mining
UR - http://www.scopus.com/inward/record.url?scp=15744376078&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2004.1399823
DO - 10.1109/ICSMC.2004.1399823
M3 - Conference contribution
AN - SCOPUS:15744376078
SN - 0780385667
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 1383
EP - 1387
BT - 2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
T2 - 2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Y2 - 10 October 2004 through 13 October 2004
ER -