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首页> 外文期刊>Knowledge-Based Systems >Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems
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Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems

机译：不完整邻域决策系统中使用Lebesgue和熵测度的基于邻域多粒度粗糙集的属性约简

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For incomplete data with mixed numerical and symbolic attributes, attribute reduction based on neighborhood multi-granulation rough sets (NMRS) is an important method to improve the classification performance. However, most classical attribute reduction methods can only handle finite sets as to produce more attributes and lower classification accuracy. This paper proposes a novel NMRS-based attribute reduction method using Lebesgue and entropy measures in incomplete neighborhood decision systems. First, some concepts of optimistic and pessimistic NMRS models in incomplete neighborhood decision systems are given, respectively. Then, a Lebesgue measure is combined with NMRS to study neighborhood tolerance class-based uncertainty measures. To analyze the uncertainty, noise and redundancy of incomplete neighborhood decision systems in detail, some neighborhood multi-granulation entropy-based uncertainty measures are developed by integrating Lebesgue and entropy measures. Inspired by both algebraic view with information view in NMRS, the pessimistic neighborhood multi-granulation dependency joint entropy is proposed. What is more, the corresponding properties are further deduced and the relationships among these measures are discussed, which can help to investigate the uncertainty of incomplete neighborhood decision systems. Finally, the Fisher linear discriminant method is used to eliminate irrelevant attributes to significantly reduce computational complexity for high-dimensional datasets, and a heuristic attribute reduction algorithm with complexity analysis is designed to improve classification performance of incomplete and mixed datasets. Experimental results under seven UCI datasets and eight gene expression datasets illustrate that the proposed method is effective to select most relevant attributes with higher classification accuracy, as compared with representative algorithms. (C) 2019 Elsevier B.V. All rights reserved.
机译：对于具有混合数值和符号属性的不完整数据，基于邻域多粒度粗糙集（NMRS）的属性约简是提高分类性能的重要方法。但是，大多数经典的属性约简方法只能处理有限集，以产生更多的属性并降低分类精度。本文提出了一种在不完整邻域决策系统中使用Lebesgue和熵测度的基于NMRS的属性约简方法。首先，给出了不完整邻域决策系统中乐观和悲观NMRS模型的一些概念。然后，将Lebesgue测度与NMRS结合起来研究基于邻域容忍度的不确定性测度。为了详细分析不完备邻域决策系统的不确定性，噪声和冗余度，结合Lebesgue和熵测度，提出了一些基于邻域多粒度熵的不确定性度量。受NMRS中的代数视图和信息视图的启发，提出了悲观邻域多粒度依赖联合熵。此外，进一步推导了相应的属性，并讨论了这些度量之间的关系，这有助于调查不完整邻域决策系统的不确定性。最后，采用Fisher线性判别方法消除不相关的属性，以显着降低高维数据集的计算复杂度，并设计了一种具有复杂度分析的启发式属性约简算法，以提高不完整和混合数据集的分类性能。在七个UCI数据集和八个基因表达数据集下的实验结果表明，与代表性算法相比，该方法可有效地以较高的分类精度选择最相关的属性。 （C）2019 Elsevier B.V.保留所有权利。

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