基于增量互信息和蟑螂群优化的特征选择算法
Feature selection algorithm based on incremental mutual information and cockroach swarm optimization
AI总结：
针对高维大规模数据集中互信息特征选择计算成本高且易选冗余特征的问题，提出基于增量互信息的改进蟑螂群优化算法（IMIICSO），利用粗糙集理论和决策表约简知识指导搜索，有效降低特征子集基数并保持高准确率。
摘要：
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，粗糙集理论提供了许多度量，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不准确，特别是在超样本实例中，在高维超高维数据集中这无疑是一个 NP-hard 问题。尽管目前已经提出了许多具有代表性的群智能算法特征选择策略来提高准确性，但在使用这些特征选择算法处理高维大规模数据集时仍然存在瓶颈，这会消耗大量性能，并且容易选择弱相关和冗余的特征。在本研究中，我们提出了一种基于增量互信息的改进群智能优化方法（IMIICSO），该方法使用粗糙集理论来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算法的全局搜索。通过探索超样本的互信息计算，我们不仅可以丢弃无用的特征以加快内部和外部计算，还可以使用 IMIICSO 方法有效减少最优特征子集的基数，使得通过比较基数最小化。基于增量互信息的改进蟑螂群算法选择的特征子集的准确性优于或几乎与原始群智能优化算法相同。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出算法的效率和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
