Generation of reducts and threshold functions using discernibility and indiscerniblity matrices
Generation of reducts and threshold functions using discernibility and indiscerniblity matrices
复制标题
使用可辨别性和不可辨别性矩阵生成归约和阈值函数
DOI：
10.1109/sera.2017.7965707
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Toyoshiro Nakashima
中科院分区：
文献类型：
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作者：
N. Ishii;Ippei Torii;K. Iwata;Kazuya Odagiri;Toyoshiro Nakashima
数据降维是数据处理中的一个重要问题，在应用领域中对高维数据的分析需要进行降维。粗糙集理论是将高维数据约简为低维数据的基础和有效手段。粗糙集中的约简是特征的最小子集，它与高维模式中的整个特征具有相同的可分辨能力。本文提出的最小距离最近邻关系为分类提供了基础信息。最近邻关系在基于可约性和不可约性矩阵的布尔推理中起着重要作用，本文提出了不可约性矩阵来检验约简和阈值函数的充分条件。在此基础上，提出了基于最近邻关系的约简和阈值函数的生成方法，并利用布尔运算对不可约性矩阵和不可约性矩阵进行处理。
Dimension reduction of data is an important issue in the data processing and iti is needed for the analysis of higher dimensional data in the application domain. Rough set is fundamental and useful to reduce higher dimensional data to lower one. Reduct in the rough set is a minimal subset of features, which has the same discernible power as the entire features in the higher dimensional scheme. The nearest neighbor relation with minimal distance proposed here has a fundamental information for classification. The nearest neighbor relation plays a fundamental role for generation of reducts and threshold functions using the Boolean reasoning on the discernibility and in discernibility matrices, in which the indiscernibility matrix is proposed here to test the sufficient condition for reduct and threshold function. Then, genetation methods for the reducts and threshold functions based on the nearest neighbor relation are proposed here using Boolean operations on the discernibility and the indiscernibility matrices.
控制变量可以自信地减少 kNN 分类
DOI：
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发表时间：
2011
期刊：
KES11, Lecture Notes in Computer Science Springer
影响因子：
--
作者：
Naohiro Ishii;Yuichi Morioka;Yonngguang Bao;Hidekazu Tanaka
通讯作者：
Hidekazu Tanaka
改进的归约-最近邻分类
DOI：
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发表时间：
2012
期刊：
Proc.IEEE/ACIS Int.Conf.on Computer & Inf.Science IEEE Computer Society Pub.
影响因子：
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作者：
Naohiro Ishii;I.Torii;他2名
通讯作者：
他2名