An evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets

Aliferis CF, Cooper GF. An evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (1994) 8–14.   PMID:  7950017

Bayesian learning of belief networks (BLN) is a method for automatically constructing belief networks (BNs) from data using search and Bayesian scoring techniques. K2 is a particular iustantiation of the method that implements a greedy search strategy. To evaluate the accuracy of K2, we randomly generated a number of BNs and for each of those we simulated data sets. K2 was then used to induce the generating BNs from the simulated data. We examine the performance of the program, and the factors that influence it. We also present a simple BN model, developed from our results, which predicts the accuracy of K2, when given various characteristics of the data set

Publication Year: 
1994
Faculty Author: 
Publication Credits: 
Aliferis CF, Cooper GF.
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