K2 & HC Structure Learning Algorithm
WU Yongguang
PANG Shichun
Abstract：Bayesian network plays an important role in the field of artificial intelligence .The capability of learning knowledge from data makes it develop rapidly in medicine ,fault diagnosis ,forecasting and other fields .Structure learning al-gorithm of Bayesian network becomes an important research area ,which can effectively analyze dependencies between varia-bles and discover knowledge and data properly .Hill-Climbing strategy can reduce the complex solution space and improve the performance of structure learning algorithm .At the same time ,the K2 algorithm is outstanding on the performance of sco-ring .Combined the scoring function of K2 with the efficient Hill-Climbing strategy ,the K2&HC algorithm is proposed . Meanwhile ,Backtracking principle integrates into the search strategy to solve the problem about the structure learning algo-rithm converging to a local optimum ,which can optimize the performance of the algorithm .Contrast with K2 and K2SA sim-ulation ,the conclusions are made that K2&HC algorithm is outstanding on the comprehensive performance of the accuracy and convergence rate .
Keywords：K2 algorithmhill-climbingsearch-and-scoreBayesian networkstructure learning
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1137-1140,1145 )