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A knowledge granularity based heuristic algorithm for attribute reduction

机译：基于知识粒度的基于颗粒度的属性减少的启发式算法

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摘要

Rough set theory is a new mathematical tool to deal with the imprecise, incomplete and inconsistent data. Attribute reduction is one of important parts in rough set theory. Currently, lots of literatures have proposed many algorithms for attribute reduction based on similarity. But all these algorithms just consider the connection of condition attributes and decision attributes, and the similarity of condition attributes is neglected. A heuristic algorithm for attribute reduction based on knowledge granularity is proposed. Firstly, we calculate the similarity between condition attribute and decision attribute, and then use the similarity between different conditions attributes to measure and choose important attributes which are added to the reduction set. Theoretical analysis and experiments show that the algorithm of this paper is efficient and feasible.
机译：粗糙集理论是一种处理不精确，不完整和不一致的数据的新数学工具。 属性减少是粗糙集理论中的重要部分之一。 目前，许多文献已经提出了许多基于相似性的属性减少的算法。 但所有这些算法只需考虑条件属性和决策属性的连接，并且忽略了条件属性的相似性。 提出了一种基于知识粒度的属性降低的启发式算法。 首先，我们计算条件属性和决策属性之间的相似性，然后使用不同条件之间的相似性属性来测量并选择添加到缩减集的重要属性。 理论分析和实验表明，本文的算法是有效可行的。

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