For heterogeneous data sets containing numerical and symbolic feature values, feature selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very significant step to preprocess data and improve its classification performance. This article presents an FNMRS-based feature selection approach in neighborhood decision systems. First, some concepts of fuzzy neighborhood rough sets and neighborhood multigranulation rough sets are given, and then the FNMRS model is investigated to construct uncertainty measures. Second, the optimistic and pessimistic FNMRS models are built by using fuzzy neighborhood multigranulation lower and upper approximations from algebra view, and some fuzzy neighborhood entropy-based uncertainty measures are developed in information view. Inspired by both algebra and information views based on the FNMRS model, the fuzzy neighborhood pessimistic multigranulation entropy is proposed. Third, the Fisher score model is utilized to delete irrelevant features to decrease the complexity of high-dimensional data sets, and then, a forward feature selection algorithm is provided to promote the performance of heterogeneous data classification. Experimental results on 12 data sets show that the presented model is effective for selecting important features with the higher stability of classification in neighborhood decision systems.

中文翻译：

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使用基于模糊邻域熵的不确定性测度对模糊邻域多粒度粗糙集进行特征选择


对于包含数值和符号特征值的异构数据集，基于模糊邻域多粒度粗糙集（FNMRS）的特征选择是预处理数据和提高其分类性能的一个非常重要的步骤。本文提出了邻域决策系统中基于 FNMRS 的特征选择方法。首先给出了模糊邻域粗糙集和邻域多粒度粗糙集的一些概念，然后研究了FNMRS模型来构造不确定性测度。其次，从代数角度利用模糊邻域多粒度下近似和上近似建立了乐观和悲观的FNMRS模型，并从信息角度提出了一些基于模糊邻域熵的不确定性测度。受基于FNMRS模型的代数和信息视图的启发，提出了模糊邻域悲观多粒熵。第三，利用Fisher评分模型删除不相关的特征以降低高维数据集的复杂性，然后提供前向特征选择算法以提高异构数据分类的性能。在12个数据集上的实验结果表明，该模型对于邻域决策系统中分类稳定性较高的重要特征选择是有效的。