基于联合粒度属性约简信息损失的研究

Research on Information Loss of Attribute Reduction Based on Joint Granularity

摘要: 随着互联网技术的迅速发展，社会进入了大数据时代。数据不仅类型多种多样，结构错综复杂还具有动态变化的特点。如何从海量数据中快速获取有价值的信息是当前亟待解决的问题。粗糙集是一种处理数据不确定性的数据评价方法。属性约简是粗糙集理论的一个重要核心应用。本文将围绕属性约简后信息损失量进行研究，从而找寻一种属性约简算法，在约简后既能保持数据分类准确率较高且信息损失较少。本文借助知识粒度的概念和约简算法，引入联合粒度，并将其运用到属性约简过程，进一步得出基于联合粒度属性约简算法。然后运用其算法对决策表系统进行约简，得出该算法在保持分类准确率不变的情况下，其信息损失量降至较低。最后通过UCI数据集进行仿真实验探究，从而验证了该方法的准确性和有效性。

Abstract: With the rapid development of Internet technology, the society has entered the era of big data. The data is not only of various types and structures, but also of dynamic change. How to quickly obtain valuable information from massive data is an urgent problem to be solved. Rough set is a data evaluation method to deal with data uncertainty. Attribute reduction is an important core application of rough set theory. This paper will focus on the amount of information loss after attribute reduction, so as to find an attribute reduction algorithm, which can keep the data classification accuracy higher and information loss less after reduction. In this paper, the concept of knowledge granularity and reduction algorithm, the introduction of joint granularity, and its application to the process of attribute reduction, further get the attribute reduction algorithm based on joint granularity. Then the algorithm is used to reduce the decision table system. It is concluded that the information loss of the algorithm is reduced to a low level while the classification accuracy remains unchanged. Finally, the accuracy and effectiveness of this method are verified by the simulation experiment of UCI data set.