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Approachs to Computing Maximal Consistent Block
Communications in Computer and Information Science, 2014 Maximal consistent block is a technique for rule acquisition in incomplete information systems. It was first proposed by Yee Leung and Deyu Li in 2001. However, the maximal consistent blocks of an incomplete information system must be computed before they are put into use. In this paper, we introduced several approaches for computing maximal consistent
Xiangrui Liu, Mingwen Shao
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Uncertainty Measure of Knowledge and Rough Set Based on Maximal Consistent Block Technique
2007 International Conference on Machine Learning and Cybernetics, 2007 In incomplete information systems, similarity measures or tolerance relations replace indiscernible relations, and the corresponding similarity or tolerance classes form coverage instead of classification of Universe. On the other hand, without satisfying the properties of transference and symmetry, there may have misjudgments in tolerance or ...
Xue-Gang Hu
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Maximal consistent block technique for rule acquisition in incomplete information systems
Information Sciences, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yee Leung, Deyu Li
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Multicost Decision-Theoretic Rough Sets Based on Maximal Consistent Blocks
Lecture Notes in Computer Science, 2014Decision-theoretic rough set comes from Bayesian decision procedure, in which a pair of the thresholds is derived by the cost matrix for the construction of probabilistic rough set. However, classical decision-theoretic rough set can only be used to deal with complete information systems.
Xibei Yang, Yong Qi, Xiaoning Song
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International Journal of Machine Learning and Cybernetics, 2023
Wei-Zhi Wu, Wu Wei-Zhi
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Wei-Zhi Wu, Wu Wei-Zhi
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Knowledge-Based Systems, 2021
Abstract Decision-making is the most important business activity and becomes more complex in the current big-data situation. Most organizational decision-making is made in a group and has the data analytics function to seek specific answers for specific purposes.
Yan Sun +3 more
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Abstract Decision-making is the most important business activity and becomes more complex in the current big-data situation. Most organizational decision-making is made in a group and has the data analytics function to seek specific answers for specific purposes.
Yan Sun +3 more
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Information Sciences, 2021
In this paper we discuss incomplete data sets with missing attribute values interpreted as “do not care” conditions. For data mining, we use two types of probabilistic approximations, global and saturated. Such approximations are constructed from two types of granules, characteristic sets and maximal consistent blocks. We present results of experiments
Patrick G. Clark +3 more
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In this paper we discuss incomplete data sets with missing attribute values interpreted as “do not care” conditions. For data mining, we use two types of probabilistic approximations, global and saturated. Such approximations are constructed from two types of granules, characteristic sets and maximal consistent blocks. We present results of experiments
Patrick G. Clark +3 more
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Characteristic Sets and Generalized Maximal Consistent Blocks in Mining Incomplete Data
Information Sciences, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Patrick G. Clark +3 more
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International Journal of Approximate Reasoning
Bin Pang, Ju-Sheng Mi, Wei-Zhi Wu
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Bin Pang, Ju-Sheng Mi, Wei-Zhi Wu
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Knowledge Granulation in Interval-Valued Information Systems Based on Maximal Consistent Blocks
2014Rough set theory, proposed by Pawlak in the early 1980s, is an extension of the classical set theory for modeling uncertainty or imprecision information. In this paper, we investigate partial relations and propose the concept of knowledge granulation based on the maximal consistent block in interval-valued information systems. The knowledge granulation
Nan Zhang 0041, Xiaodong Yue 0002
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