Feature selection is an effective preprocessing technique to reduce data dimension. For feature selection, rough set theory provides many measures, among which mutual information is one of the most important attribute measures. However, mutual information based importance measures are computationally expensive and inaccurate, especially in hypersample instances, and it is undoubtedly a NP-hard problem in high-dimensional hyperhigh-dimensional data sets. Although many representative group intelligent algorithm feature selection strategies have been proposed so far to improve the accuracy, there is still a bottleneck when using these feature selection algorithms to process high-dimensional large-scale data sets, which consumes a lot of performance and is easy to select weakly correlated and redundant features. In this study, we propose an incremental mutual information based improved swarm intelligent optimization method (IMIICSO), which uses rough set theory to calculate the importance of feature selection based on mutual information. This method extracts decision table reduction knowledge to guide group algorithm global search. By exploring the computation of mutual information of supersamples, we can not only discard the useless features to speed up the internal and external computation, but also effectively reduce the cardinality of the optimal feature subset by using IMIICSO method, so that the cardinality is minimized by comparison. The accuracy of feature subsets selected by the improved cockroach swarm algorithm based on incremental mutual information is better or almost the same as that of the original swarm intelligent optimization algorithm. Experiments using 10 datasets derived from UCI, including large scale and high dimensional datasets, confirmed the efficiency and effectiveness of the proposed algorithm.

翻译：特征选择是降低数据维度的有效预处理技术。在特征选择中，粗糙集理论提供了多种度量方法，其中互信息是最重要的属性度量之一。然而，基于互信息的重要性度量计算成本高且精度不足，尤其在超样本实例中，对于高维超大数据集而言，这无疑是一个NP难问题。尽管目前已有许多代表性的群体智能算法特征选择策略用于提升精度，但在处理高维大规模数据集时，这些特征选择算法仍存在瓶颈，不仅消耗大量性能，而且容易选择弱相关和冗余特征。本研究提出了一种基于增量互信息的改进群体智能优化方法（IMIICSO），该方法利用粗糙集理论计算基于互信息的特征选择重要性。该方法提取决策表约简知识以指导群体算法的全局搜索。通过探索超样本互信息的计算，我们不仅可以丢弃无用特征以加速内外计算，还能利用IMIICSO方法有效降低最优特征子集的基数，使其最小化。基于增量互信息的改进蟑螂群算法所选择的特征子集，其精度优于或几乎等同于原始群体智能优化算法。使用来自UCI的10个数据集（包括大规模和高维数据集）进行的实验，证实了所提算法的效率和有效性。