一种基于模糊粗糙集的快速特征选择算法

模糊粗糙集由于能够处理实数值数据，甚至是混合值数据中的不确定性受到人们的广泛关注,其最重要的应用之一是特征选择,相关的特征选择方法已有不少研究,但其快速的特征选择算法研究很少。实际中的数据一般含有噪声点或信息含量低的样例,如果对数据集先筛选出代表样例,再对筛选的样例集进行数据挖掘便会降低挖掘计算量。本文基于模糊粗糙集，先根据样例的模糊下近似值对样例进行筛选,然后利用筛选样例的模糊粗糙信息熵构造特征选择的评估度量,并给出相应的特征选择算法,从而降低了算法的计算复杂度。数值试验表明该快速算法具有有效性,并且对控制筛选样例个数的参数给出了建议。

Fast Feature Selection Algorithm Based on Fuzzy Rough Sets

Fuzzy rough set theory has been paid much attention since it can be used to deal with the uncertainty in the real-valued data or even the mixed data. One of the most important applications of fuzzy rough sets is feature selection, and there have existed many related feature selection methods. However, little attention has been paid on fast feature selection algorithms. Data collected in practice generally include noises or possess some instances with less information. Considering to previously select representative instances from the original data set and perform data mining algorithms on the selected instances set, one may reduce the computation of the algorithms. In view of the advantage of instance selection, the instances are firstly selected based on fuzzy rough sets according to the values of the fuzzy lower approximation of instances in this paper. Then, the evaluation measure of feature selection is constructed by using fuzzy rough set-based information entropy of the selected instances, and the corresponding feature selection algorithm is provided to alleviate the computational complexity. Some numerical experiments are conducted to show the efficiency of the proposed fast algorithm, and the reasonable suggestion of the critical parameter is given to determine the number of the selected instances.