目标融合系统中属性离散化的研究

Xiangyu Meng, Rong Cong, Kai Li
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引用次数: 3

摘要

本文从属性的重要性出发,探讨了一种新的连续属性离散化方法,克服了传统粗糙集方法的局限性。根据一致性程度进行分组是选择候选切点的有效方法,也有助于减少切点的数量。以属性离散化的形式保持了决策系统的一致性,减少了截点数,提高了效率。采用变精度粗信息熵作为测量标准,对噪声有良好的容忍度。实验表明,该算法得到了较好的约简结果。
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Research on attributes discretization in target fusion syetem
A new method for discretization of continuous attributes is trying to be discussed in this paper based on the importance of attributes that finds solutions to overcome the limitation of the traditional rough sets. According to consistency degrees, grouping is an effective way to select candidate cut points, it also helps reducing the numbers of cut points. So the consistency of the decision-making system is maintained in the form of attribute discretization which permits the reduction of cut point numbers and the improvement of efficiency. Adopting variable precision rough information entropy as measuring criterion, it has a good tolerance to noise. Experiments show that the algorithm yields satisfy this reduction results.
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