基于粒度粗糙熵与改进蜂群算法的特征选择

经典的人工蜂群(artificial bee colony, ABC)算法面临着收敛速度慢、易陷入局部最优等不足,因此基于该算法来进行特征选择还存在很多问题.对此,提出了一种基于粒度粗糙熵与改进蜂群算法的特征选择方法FS＿GREIABC.首先,将粗糙集中的知识粒度与粗糙熵有机地结合起来,提出一种新的信息熵模型——粒度粗糙熵;其次,将粒度粗糙熵应用于ABC算法中,提出一种基于粒度粗糙熵的适应度函数,从而获得了一种新的适应度计算策略;第三,为了提高ABC算法的局部搜索能力,将云模型引入到跟随蜂阶段.在多个UCI数据集以及软件缺陷预测数据集上的实验表明,相对于现有的特征选择算法, FS＿GREIABC不仅能够选择较少的特征,而且具有更好的分类性能.

Feature Selection Based on Granularity of Knowledge Rough Entropy and Improved Artificial Bee Colony Algorithm

The classical artificial bee colony (ABC) algorithm is also faced with slow convergence speed, and it is easy to fall into local optimality, so there are still many problems in feature selection based on this algorithm. Therefore, a feature selection method based on the rough entropy of granularity and an improved bee colony algorithm, namely FS_GREIABC, is proposed. Firstly, a new information entropy model, namely the rough entropy of granularity, is proposed by combining the knowledge granularity and the rough entropy in the rough set. Secondly, the rough entropy of granularity is applied to the ABC algorithm, and a fitness function based on the rough entropy of granularity is proposed, so as to obtain a new fitness calculation strategy. Thirdly, in order to improve the local search ability of the ABC algorithm, a cloud model is introduced into the following bee stage. Experiments on multiple UCI datasets and software defect prediction datasets show that FS_GREIABC not only selects fewer features but also has better classification performance than the existing feature selection algorithms.