CAJ | 학술논문

贝叶斯网络理论在人工智能领域发挥着重要作用。贝叶斯网络从数据中学习知识的能力使得它在医学、故障诊断、预测等领域的应用迅速发展起来。结构学习算法成为贝叶斯网络的重要研究方向，它能够有效分析变量之间依赖关系，合理挖掘数据和知识。K2算法评分性能突出，而爬山算法能有效弥补K2评分法的解空间过于复杂的问题。论文结合K2评分函数和爬山策略，提出了K2&HC算法。同时，K2&HC算法在爬山策略中融入了回溯原理，解决了贝叶斯结构学习算法中存在的收敛于局部最优的问题，合理优化了算法的性能。同K2和K2SA算法进行仿真对比，得出在精度和收敛速度综合性能上K2&HC表现突出的结论。
패협사망락이론재인공지능영역발휘착중요작용。패협사망락종수거중학습지식적능력사득타재의학、고장진단、예측등영역적응용신속발전기래。결구학습산법성위패협사망락적중요연구방향，타능구유효분석변량지간의뢰관계，합리알굴수거화지식。K2산법평분성능돌출，이파산산법능유효미보K2평분법적해공간과우복잡적문제。논문결합K2평분함수화파산책략，제출료K2&HC산법。동시，K2&HC산법재파산책략중융입료회소원리，해결료패협사결구학습산법중존재적수렴우국부최우적문제，합리우화료산법적성능。동K2화K2SA산법진행방진대비，득출재정도화수렴속도종합성능상K2&HC표현돌출적결론。
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 .