Attribute reduction for sequential three-way decisions under dynamic granulation

Jin, Qian; Dang, Chuangyin; Yue, Xiaodong; Zhang, Nan

doi:10.1016/j.ijar.2017.03.009

Cited by 68 publications

(6 citation statements)

References 60 publications

(84 reference statements)

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“…Rough set was proposed by Polish scholar Pawlak in 1982 [26] and has been widely used in many fields, such as machine learning, data mining, fault diagnosis [27], and fuzzy control [28]. Its core research content includes attribute reduction [29] and classification rule reduction [30]. At present, domestic and foreign scholars mainly study it in terms of attribute reduction [31].…”

Section: Proposed Attribute Reduction Algorithmmentioning

confidence: 99%

Feature Selection Combined Feature Resolution with Attribute Reduction based on Correlation Matrix of Equivalence Classes

Junxia¹

2019

IJPE

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Feature selection is one of the key steps in text classification. To some extent, it can affect the performance of text classification. In this paper, we firstly proposed an optimized document frequency-based word frequency and document frequency and then presented the feature resolution based on the optimized document frequency. Meanwhile, we introduced rough set into feature selection and provided an attribute reduction algorithm based on the correlation matrix of equivalence classes. We finally put forward a feature selection method combining the presented feature resolution with the provided attribute reduction algorithm. The proposed feature selection method firstly employs the presented feature resolution to select some valuable text features and filter out useless terms to reduce the sparsely of text feature spaces, and then it uses the provided attribute reduction algorithm to eliminate redundant features. The comparative experimental results show that the proposed feature selection method has certain advantages in consumed time, macro-average, micro-average, and average classification accuracy.

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Section: Proposed Attribute Reduction Algorithmmentioning

confidence: 99%

Feature Selection Combined Feature Resolution with Attribute Reduction based on Correlation Matrix of Equivalence Classes

Junxia¹

2019

IJPE

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“…Chen et al [4] established a sequential three-way decision model to deal with attribute updating in a multi-granularity universe. In summary, combining with granular computing, 3WDMs have been applied to deal with the complicated and uncertain problems in many aspects [10], [12], [27], [28], [30], [54].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Three-Way Decisions With Intuitionistic Fuzzy Numbers in Multi-Granularity Spaces

2019

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Intuitionistic fuzzy sets, as extensions of fuzzy sets, are described by intuitionistic fuzzy numbers (IFNs) that describe the uncertain concepts with the membership and non-membership degrees together. Three-way decisions are effective classification methods, which are always utilized for solving uncertain problems with fixed cost parameter values and fixed attribute values in reality. However, under dynamic intuitionistic fuzzy environments, three-way decisions face a great challenge for processing uncertain problems with IFN cost parameters and IFN attribute values. In this paper, by considering the impact of IFNs, a hierarchical three-way decision model with IFN cost parameters (H3WDIF-I) is established in multi-granularity spaces for handling dynamic and uncertain problems first. The change rules of H3WDIF-I are discussed to analyze the relationships of decisions based on the granularity structures. In addition, when the IFN cost parameters and IFN attribute values arise together, a hierarchical three-way decision model with IFNs (H3WDIF-II) is proposed to explore three-way decisions in depth under dynamic intuitionistic fuzzy environments. Similarly, the change rules of H3WDIF-II are also discussed from the viewpoint of classification losses in multi-granularity spaces. Finally, the presented change rules are verified by many examples and experiments.

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“…Qian et al [31] focused on attribute reduction for sequential three-way decisions under dynamic granulation. Wang et al [40] investigated efficient updating rough approximations with multi-dimensional variation of ordered data.…”

Section: Introductionmentioning

confidence: 99%

Related families-based attribute reduction of dynamic covering decision information systems

Lang

Cai

Fujita

et al. 2018

Knowledge-Based Systems

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Attribute reduction for sequential three-way decisions under dynamic granulation

Cited by 68 publications

References 60 publications

Feature Selection Combined Feature Resolution with Attribute Reduction based on Correlation Matrix of Equivalence Classes

Feature Selection Combined Feature Resolution with Attribute Reduction based on Correlation Matrix of Equivalence Classes

Hierarchical Three-Way Decisions With Intuitionistic Fuzzy Numbers in Multi-Granularity Spaces

Related families-based attribute reduction of dynamic covering decision information systems

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