Treated as one of the popular measures in information theory, fuzzy mutual information quantifies the amount of information that one random variable has about another one. Different from standard mutual information, fuzzy mutual information can deal with not only discrete-valued but also real-valued variables. Therefore, fuzzy mutual information has been recently used in evolutionary filter feature selection approaches to measure the correlation between the classes and the features, and the dependencies within a feature set. Typically, this way can be considered as computationally efficient but sometimes it may not contribute to the performance of a classification algorithm. To address this issue, an improved evolutionary wrapper-filter approach which integrates an initialisation scheme and a local search module based on fuzzy mutual information in differential evolution is proposed. According to a number of experiments conducted on several real-world benchmark datasets, the proposed approach does not only significantly improve the computational efficiency of an evolutionary computation technique but also the performance of a classification algorithm.

模糊互信息被视为信息论中的一种流行手段，它量化了一个随机变量对另一随机变量所具有的信息量。与标准互信息不同，模糊互信息不仅可以处理离散值，而且可以处理实值变量。因此，近来模糊互信息已用于进化过滤器特征选择方法中，以测量类与特征之间的相关性以及特征集内的依存关系。通常，这种方式可以被认为是计算效率高的，但是有时它可能对分类算法的性能没有帮助。为了解决这个问题，提出了一种改进的进化包装滤波器方法，该方法将初始化方案和基于模糊互信息的局部搜索模块集成在差分进化中。根据在几个实际基准数据集上进行的大量实验，提出的方法不仅显着提高了进化计算技术的计算效率，而且还提高了分类算法的性能。