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DISCRETIZATION OF CONTINUOUS INTERVAL-VALUED ATTRIBUTES IN ROUGH SET THEORY AND ITS APPLICATION

机译:粗糙集理论中连续区间值属性的离散化及其应用

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Rough set theory is a relatively new soft computing tool to deal with vagueness and uncertainty, and is regarded as a field of leading edge.But it cannot deal with continuous attributes, thus discretizaton is a problem which we cannot neglect Discretization based on rough set has some particular characteristics, and consistency must be satisfied for discretization of decision systems.Existing discretization methods cannot well process continuous interval-valued attributes in rough set theory.A new approach is proposed to discretize continuous interval-valued attributes in this paper, which enhances the precision of classification and accurate recognition rate in pattern recognition.In the simulation experiment, the decision table was composed of 3 features and 17 radar emitter signals, and the recognition results obtained from this discretization algorithm show that the proposed approach is valid and feasible.The approach expands the application scope of rough set theory.
机译:粗糙集理论是一种处理模糊性和不确定性的较新的软计算工具,被认为是前沿领域。但是它不能处理连续属性,因此离散化是一个我们不能忽视的问题,基于粗糙集的离散化具有现有的离散化方法不能很好地处理粗糙集理论中的连续区间值属性。本文提出了一种新的方法来对连续区间值属性进行离散化,从而增强了决策系统的离散性。在仿真实验中,决策表由3个特征和17个雷达辐射源信号组成,该离散化算法得到的识别结果表明,该方法是有效和可行的。该方法扩展了粗糙集理论的应用范围。

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