Efficient updating rough approximations with multi-dimensional variation of ordered data

Wang, Shu; Li, Tianrui; Luo, Chuan; Fujita, Hamido

doi:10.1016/j.ins.2016.08.044

Cited by 48 publications

(14 citation statements)

References 49 publications

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“…Chen et al proposed an incremental approach for update approximations of DRSA while attribute values refining or coarsening in incomplete information system [22]. Wang et al propose an incremental algorithm which can efficiently update approximations of DRSA when objects and attributes increase simultaneously [28]. Huang et al introduced a composite dominance-based rough sets model and redefined the lower and upper approximations of upward and downward unions of decision classes.…”

Section: Introductionmentioning

confidence: 99%

“…Although there are many excellent approaches of incrementally updating approximations of rough sets, they can hardly be directly applied to update approximations of rough sets under a dominance relation when the object set evolves over time except for the one and its generalization presented in [23], [28]. The incremental approach in literature [23] can not directly deal with the cases that the insertion or deletion of multiple objects at the same time.…”

Section: Introductionmentioning

confidence: 99%

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Accelerating Update of Approximations Under a Dominance Relation

Zhi-yong

2020

IEEE Access

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In real-time applications, the information system may often evolves over time. Dynamic information processing is one of main challenges in the research fields related to information processing. Rough sets theory is an excellent information processing tool which has been applied in dynamic information processing researches in recent years. Dominance-based rough sets approach is an outstanding generation of rough sets theory, which can process the ordered information related to the problems of multi-criteria decision analysis and multi-criteria sort. In this paper, we investigate some new strategies to unleash the performance of updating approximations in dominance-based rough sets approach further. An original concept of coarse boundary of approximations is given, which can be applied to prune more unnecessary computation in updating approximations further. Then a new incremental approach for updating approximations under a dominance relation is proposed and the corresponding algorithm is designed. A numeric illustration shows the feasibility of the approach. By extensive experimental estimation, the performance of the approach outperforms that of its counterpart on the computational time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accelerating Update of Approximations Under a Dominance Relation

Zhi-yong

2020

IEEE Access

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“…Recently, the incremental updating strategies have been widely researched in rough sets 7,6,19,21,20,30,4,26,38,8 . To efficiently obtain the useful acknowledge under the change of information system, we can propose the incremental updating methods to reduce the computational time in the theory of rough sets.…”

Section: Introductionmentioning

confidence: 99%

Dynamic composite decision-theoretic rough set under the change of attributes

Wang¹,

Yang²,

Chen³

et al. 2018

IJCIS

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In practical decision-making, we prefer to characterize the uncertain problems with the hybrid data, which consists of various types of data, e.g., categorical data, numerical dada, interval-valued data and set-valued data. The extended rough sets can deal with single type of data based on specific binary relation, including the equivalence relation, neighborhood relation, partial order relation, tolerance relation, etc. However, the fusion of these relations is a significant challenge task in such composite information table. To tackle this issue, this paper proposes the intersection and union composite relation, and further introduces a quantitative composite decision-theoretic rough set model. Subsequently, we present a novel matrix-based approach to compute the upper and lower approximations in proposed model. Moreover, we propose the incremental updating mechanisms and algorithms under the addition and deletion of attributes. Finally, experimental valuations are conducted to illustrate the efficiency of proposed method and algorithms.

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“…Błaszczynski and Słowinski proposed an incremental algorithm for induction rules based on the Apriori under the variable consistency DRSA 46 . Wang et al presented efficient updating rough approximations with multi-dimensional variation of ordered data 47 . In the case of variation of attributes values, Zeng et al developed dynamical updating fuzzy rough approximations for hybrid data under the variation of attribute values 48 .…”

Section: Introductionmentioning

confidence: 99%

Incremental approximation computation in incomplete ordered decision systems

Gou¹,

Wang²

2017

IJCIS

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Approximation computation is a critical step in rough sets theory used in knowledge discovery and other related tasks. In practical applications, an information system often evolves over time by the variation of attributes or objects. Effectively computing approximations is vital in data mining. Dominance-based rough set approach can handle information with preference-ordered attribute domain, but it is not able to handle the situation of data missing. Confidential Dominance-based Rough Set Approach (CDRSA) is introduced to process Incomplete Ordered Decision System (IODS). This paper focuses on incremental updating approximations under dynamic environment in IODS. With the CDRSA, the principles of incremental updating approximations are discussed while the variation of attribute sets or the union of subsets of objects and the corresponding incremental algorithms are developed. Comparative experiments on data sets of UCI and results show that the proposed incremental approaches can improve the performance of updating approximations effectively by a significant shortening of the computational time.

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Efficient updating rough approximations with multi-dimensional variation of ordered data

Cited by 48 publications

References 49 publications

Accelerating Update of Approximations Under a Dominance Relation

Accelerating Update of Approximations Under a Dominance Relation

Dynamic composite decision-theoretic rough set under the change of attributes

Incremental approximation computation in incomplete ordered decision systems

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