基于模糊粗糙集信息熵的蚁群特征选择方法

目前针对高维数据特征选择提出的启发式算法多数容易陷入局部最优，无法对整个特征空间进行有效搜索。为了提高对特征域的并行搜索能力，基于模糊粗糙集的信息熵原理，对蚁群模型的搜索策略、信息素更新和状态转移规则等进行了改进，提出蚁群特征选择方法。经UCI数据实验验证，该算法比传统的特征选择算法具有更好的选择效果，是有效的。

Ant colony feature selection based on fuzzy rough set information entropy

Most heuristic feature selection algorithms converge easily to local-best, which cannot search the whole feature space effectively. In order to improve the parallel search ability to feature space, the information entropy theory of fuzzy rough set was introduced to ant colony model, and the ant search strategy, pheromone updating and state transition rules of the model have been modified to realize ant colony model based feature selection. UCI datasets experiments indicate that the proposed algorithm is effective to feature subset selection compared with three classical feature selection algorithms.