An evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets
作者： Constantin F. Aliferis , Gregory F. Cooper
DOI: 10.1016/B978-1-55860-332-5.50006-7
关键词: Artificial intelligence 、 Simple (abstract algebra) 、 Simulated data 、 Computer science 、 Data set 、 Bayesian network 、 Greedy algorithm 、 Machine learning 、 Bayesian inference 、 Bayesian probability 、 Data mining
摘要: Bayesian learning of belief networks (BLN) is a method for automatically constructing (BNs) from data using search and scoring techniques. K2 particular iustantiation the that implements greedy strategy. To evaluate accuracy K2, we randomly generated number BNs each those simulated sets. was then used to induce generating data. We examine performance program, factors influence it. also present simple BN model, developed our results, which predicts when given various characteristics set.
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