南京大学学报(自然科学版) ›› 2021, Vol. 57 ›› Issue (2): 262271.doi: 10.13232/j.cnki.jnju.2021.02.011
• • 上一篇
Ying Sun, Tianshi Cai, Yi Zhang, Hengrong Ju, Weiping Ding()
摘要:
信息粒度和近似方法是粗糙集理论进行数据描述的两个关键.现实中数据分布情况复杂多变,现有的模型缺乏对不同数据区域进行区分的能力,且易受到异常数据的干扰,导致最终分类决策的失误.为此提出基于合理粒度的局部邻域决策粗糙集模型.首先,根据邻域中对象的个数和类别识别一些极端情况(例如离群点和标签噪声点),分别给出不同分布情况下数据点的粗糙隶属度;其次,为已识别的标签噪声数据提供一组伪标记,用伪标记对原始标签进行修正;最后引入合理粒度准则,构造由信息覆盖性函数和特殊性函数融合的新的评估标准,并通过粒子群优化算法对其进行优化,得到最佳邻域半径.实验结果表明,该方法为复杂数据处理提供了一种有效的解决方案.
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