Results 241 to 250 of about 765,630 (290)

Inconsistency guided robust attribute reduction

Information Sciences, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qu, Yanpeng, Xu, Zheng, Shang, Changjing, Ge, Xiaolong, Deng, Ansheng, Shen, Qiang   +5 more
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Attribute reduction and attribute characteristics of formal contexts

2013 International Conference on Machine Learning and Cybernetics, 2013
In this paper, by the definitions of meet-irreducible element we discuss attribute characteristics and attribute reduction of formal contexts. We first propose an effective method to determine whether an element is meet-irreducible. Then present an approach to judge the indispensable attributes and the dispensable attribute, by which the attribute ...
Eric C.C. Tsang, Ming-Wen Shao
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Improved general attribute reduction algorithms

Information Sciences, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Baizhen, Wei, Zhihua, Miao, Duoqian, Zhang, Nan, Shen, Wen, Gong, Chang, Zhang, Hongyun, Sun, Lijun   +7 more
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Attribute Reduction Based on Continuous Attribute Domain

Advanced Materials Research, 2014
Discrete data attributes reduction, there are many mature methods, but for continuous data attributes reduction, general algorithm is not very good, in real life, the continuous data feature extraction and discrete data is also important, based on the number of new brain waves as analysis object, and through the continuous eeg feature extraction ...
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Minimal Attribute Space Bias for Attribute Reduction

2007
Attribute reduction is an important inductive learning issue addressed by the Rough Sets society.Most existing works on this issue use the minimal attribute bias, i.e., searching for reducts with the minimal number of attributes. But this bias does not work well for datasets where different attributes have different sizes of domains.
Fan Min, Xianghui Du, Hang Qiu, Qihe Liu
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Test-cost-sensitive attribute reduction

Information Sciences, 2011
In many data mining and machine learning applications, there are two objectives in the task of classification; one is decreasing the test cost, the other is improving the classification accuracy. Most existing research work focuses on the latter, with attribute reduction serving as an optional pre-processing stage to remove redundant attributes.
Fan Min, Huaping He, Yuhua Qian, William Zhu   +3 more
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Attribute reduction based on fusion information entropy

International Journal of Approximate Reasoning, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ji, Xia, Li, Jie, Yao, Sheng, Zhao, Peng
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Multi-granularity Attribute Reduction

2018
It is known that different parameters used in Gaussian kernel will provide us different granularities of information granulations. Therefore, kernel based fuzzy rough set has the characteristic of multi-granularity. From this point of view, a multi-granularity attribute reduction strategy is developed in this paper. Different from traditional reduction
Shaochen Liang, Keyu Liu, Xiangjian Chen, Pingxin Wang, Xibei Yang   +4 more
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Universal Attribute Reduction Problem

2007
In the paper, some generalizations of the notions of reduct and test (superreduct) are considered. The accuracy of greedy algorithm for construction of partial test is investigated. A lower bound on the minimal cardinality of partial reducts based on an information on greedy algorithm work is studied.
Mikhail Ju. Moshkov, Marcin Piliszczuk, Beata Zielosko   +2 more
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Research on attribute reduction algorithm

2010 International Conference On Computer Design and Applications, 2010
In this paper, by the comprehension and analysis of data mining algorithm based on tradition rough set theory, on improved discernibility matrix, the information for core and reduction without inclusion relation is gained in the process of comparing objects. Based on this information, a new heuristic algorithm for reduction is proposed.
Qiang Lv, Liang-shan Shao
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