首页> 外文期刊>IEEE Transactions on Systems, Man, and Cybernetics >ADPDF: A Hybrid Attribute Discrimination Method for Psychometric Data With Fuzziness
【24h】

ADPDF: A Hybrid Attribute Discrimination Method for Psychometric Data With Fuzziness

机译:ADPDF:具有模糊性的心理数据的混合属性区分方法

获取原文
获取原文并翻译 | 示例

摘要

The existing approaches for attribute discrimination are applied to clinical data with unambiguous boundaries, and rarely take into careful consideration on how to utilize psychometric data with fuzziness. In addition, it is difficult for conventional attribute reduction methods to reduce attributes of psychometric data which are composed of a lot of attributes and contain a relatively small-scale samples. Importantly, these methods cannot be used to reduce options which are relevant to each other. In this paper, we first introduce new concepts, that is, option entropy and option influence degree, which are employed to describe the relation and distribution of options. Then, we propose a hybrid attribute discrimination method for psychometric data with fuzziness, called a hybrid attribute discrimination for psychometric data with fuzziness (ADPDF). ADPDF contains three essential techniques: 1) a fuzzy option reduction method, which aims to combine a fuzzy option to adjacent options, and is used to reduce the fuzziness of options in a psychometry and 2)${k}$-fold attribute reduction method, which partitions all samples into several subsets and negotiates the reduction results of different subsets, and reduces the noise for the purpose of accurately discovering key attributes. In order to show the advantages of the proposed approach, we conducted experiments on two real datasets collected from clinical diagnoses. The experimental results show that the proposed method can decrease the correlation between options effectively. Interestingly, we find three reserved options and one hundred samples in each subset show the best classification performance. Finally, we compare the proposed method with typical attribute discrimination algorithms. The results reveal that our method can improve the classification accuracy with the guarantee of time performance.
机译:现有的属性判别方法被应用于具有明确界限的临床数据,并且很少仔细考虑如何利用带有模糊性的心理测量数据。另外,传统的属性约简方法难以减少由许多属性组成并且包含相对较小样本的心理测量数据的属性。重要的是,这些方法不能用于减少彼此相关的选项。在本文中,我们首先介绍了期权熵和期权影响度的新概念,它们被用来描述期权的关系和分布。然后,我们提出了一种针对具有模糊性的心理数据的混合属性判别方法,称为一种针对具有模糊性的心理数据的混合属性判别方法(ADPDF)。 ADPDF包含三种基本技术:1)模糊选项减少方法,旨在将模糊选项与相邻选项组合在一起,并用于减少心理测量中选项的模糊性; 2) n <内联公式xmlns:mml = “ http://www.w3.org/1998/Math/MathML ” xmlns:xlink = “ http://www.w3.org/1999/xlink ”> $ {k} $ n-fold属性归约方法,该方法将所有样本划分为几个子集,并协商不同子集的归约结果,并为准确发现关键属性的目的。为了显示该方法的优势,我们对从临床诊断中收集的两个真实数据集进行了实验。实验结果表明,该方法可以有效地降低期权之间的相关性。有趣的是,我们发现三个保留选项,每个子集中的一百个样本显示出最佳的分类性能。最后,我们将提出的方法与典型的属性识别算法进行了比较。结果表明,该方法可以在保证时间性能的同时提高分类精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号