Knowledge reduction in interval-valued information systems

Miao, Duoqian; Zhang, Nan; Yue, Xiaodong

doi:10.1109/coginf.2009.5250721

Cited by 15 publications

(5 citation statements)

References 22 publications

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“…Leung et al [9] and Miao et al [17] considered the problem of attribute reduction and rule learning in interval-valued information tables. Sun et al [32] considered attribute reduction in interval-valued fuzzy information tables.…”

Section: Studies Related To Interval Setsmentioning

confidence: 99%

Interval sets and interval-set algebras

Yao

2009

2009 8th IEEE International Conference on Cognitive Informatics

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An interval set is an interval in the power set lattice based on a universal set and is a family of subsets of the universal set. Interval sets and interval-set algebras provide a tool for modeling and processing partially known concepts and for approximating undefinable or complex concepts. Existing results on interval sets and interval-set algebras are reviewed and new results are given. Two types of interval-set algebras are examined based on an inclusion ordering and a knowledge ordering, respectively. Related studies are summarized.

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Section: Studies Related To Interval Setsmentioning

confidence: 99%

Interval sets and interval-set algebras

Yao

2009

2009 8th IEEE International Conference on Cognitive Informatics

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“…By using maximal consistent blocks in incomplete information systems, Leung et al [31] proposed a more efficient computational method for attribute reduction. Miao et al [32] introduced an attribute reduction method which can leave maximal consistent blocks unchanged in an interval-valued information system. To obtain the reducts, with respect to one decision class instead of all decision classes, Liu et al [33] constructed discernibility matrices regarding lth lower approximation reduction, lth decision class reduction, and β-reduction of the lth decision class.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation

Zhang

Gao

2019

Applied Sciences

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Attribute reduction is a challenging problem in rough set theory, which has been applied in many research fields, including knowledge representation, machine learning, and artificial intelligence. The main objective of attribute reduction is to obtain a minimal attribute subset that can retain the same classification or discernibility properties as the original information system. Recently, many attribute reduction algorithms, such as positive region preservation, generalized decision preservation, and distribution preservation, have been proposed. The existing attribute reduction algorithms for generalized decision preservation are mainly based on the discernibility matrix and are, thus, computationally very expensive and hard to use in large-scale and high-dimensional data sets. To overcome this problem, we introduce the similarity degree for generalized decision preservation. On this basis, the inner and outer significance measures are proposed. By using heuristic strategies, we develop two quick reduction algorithms for generalized decision preservation. Finally, theoretical and experimental results show that the proposed heuristic reduction algorithms are effective and efficient.

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“…By introducing the concept of misclassification rate between two interval numbers, Leung et al proposed a tolerancebased rough set model for extracting classification rules from IvIS [14]. Based on the similarity rate between two interval values, Miao et al extended the concept of original maximal consistent blocks to formulate the rough approximations in IvIS [21]. In virtue of interval-inclusion degree, Zhang et al introduced a binary neighborhood relation on an IvIS as well as the concept of interval-valued granular rules and proposed a confidence-preserved attribute reduction approach to extract compact decision rules [44].…”

Section: Introductionmentioning

confidence: 99%

Incremental updating of rough approximations in interval-valued information systems under attribute generalization

Zhang

Luo

et al. 2016

Information Sciences

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Interval-valued Information System (IvIS) is a generalized model of single-valued information system, in which the attribute values of objects are all interval values instead of single values. The attribute set in IvIS is not static but rather dynamically changing over time with the collection of new information, which results in the continuous updating of rough approximations for rough set-based data analysis. In this paper, on the basis of the similarity-based rough set model in IvIS, we develop incremental approaches for updating rough approximations in IvIS under attribute generalization, which refers to the dynamic changing of attributes. Firstly, increment relationships between the original rough approximations and the updated ones when adding or deleting an attribute set are analyzed, respectively. And the incremental mechanisms for updating rough approximations in IvIS are introduced, which carry out the computation using the previous results from the original data set along with new results. Then, the corresponding incremental algorithms are designed based on the proposed mechanisms. Finally, comparative experiments on data sets from UCI as well as artificial data sets are conducted, respectively. Experimental results show that the proposed incremental algorithms can effectively reduce the running time for the computation of rough approximations in comparison with the static algorithm.

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Knowledge reduction in interval-valued information systems

Cited by 15 publications

References 22 publications

Interval sets and interval-set algebras

Interval sets and interval-set algebras

Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation

Incremental updating of rough approximations in interval-valued information systems under attribute generalization

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