Abstract In rough set philosophy, each set of data can be seen as a fuzzy decision table. Since a decision table dynamically increases with time and space, these decision tables are integrated into a new one called fused decision table. In this paper, we focus on designing an incremental feature selection method on fused decision table by optimizing the space constraint of storing discernibility matrix. Here discernibility matrix is a known way of discernibility information measure in rough set theory. This paper applies the quasi/pseudo value of discernibility matrix rather than the true value of discernibility matrix to design an incremental mechanism. Unlike those discernibility matrix based non-incremental algorithms, the improved algorithm needs not save the whole discernibility matrix in main memory, which is desirable for the large data sets. More importantly, with the increment of decision tables, the discernibility matrix-based feature selection algorithm could constrain the computational cost by applying efficient information updating techniques—quasi/pseudo approximation operators. Finally, our experiments reveal that the proposed algorithm needs less computational cost, especially less occupied space, on the condition that the accuracy is limitedly lost.

中文翻译：

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基于可辨识度矩阵的融合决策表增量特征选择

摘要 在粗糙集哲学中，每组数据都可以看作是一个模糊决策表。由于决策表随时间和空间动态增加，因此将这些决策表整合为一个新的称为融合决策表的决策表。在本文中，我们通过优化存储辨别矩阵的空间约束来设计一种融合决策表的增量特征选择方法。这里的区分矩阵是粗糙集理论中一种已知的区分信息度量方法。本文应用可识别矩阵的准/伪值而不是可识别矩阵的真值来设计增量机制。与那些基于识别矩阵的非增量算法不同，改进的算法不需要将整个识别矩阵保存在主存储器中，这对于大数据集是可取的。更重要的是，随着决策表的增加，基于辨别矩阵的特征选择算法可以通过应用有效的信息更新技术——准/伪近似算子来约束计算成本。最后，我们的实验表明，该算法在精度损失有限的情况下需要更少的计算成本，尤其是占用空间更少。