用于特征选择的BF算法及其与BF算法的比较

借助于人工智能搜索技术,Xu等人提出了计算量优于B&B算法的全局最优特征提取BF*算法。本文在分析了BF*算法搜索树T_B结构的基础上,提出了一种比T_B具有更少节点的搜索树T_b及相应的BF**算法。并证明,在不另增加存贮量和保持全局最优特性的前提下,BF**算法在计算量方面优于BF*算法。

BF Algorithm for Feature Selection and Its Comparison with BF Algorithm

With AI technology in searching, Xu, et al. presented a global optimum feature selection algorithm, BF, which is computationally superior to B&B one. Based on the analysis of search tree TB used in BF, this paper proposes a new structure of search tree, Tb, on which there are fewer nodes than on TB. A feature selection algorithm on Tb, referred as to BF, is designed, of which the computational complexity is lower than BF's, without increasing storage and with global optimum property.