Attribute reduction in decision‐theoretic rough set model based on minimum decision cost

Bi, Zhongqin; Xu, Feifei; Lei, Jingsheng; Jiang, Teng

doi:10.1002/cpe.3830

Cited by 9 publications

(3 citation statements)

References 51 publications

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“…What's more, the RS theory reduces attributes based on the partition of equivalence relation, which can be accomplished through the evaluation metric [30,31], like mutual information and information entropy [10,38]. In general, the RS theory achieves better performance on the categorical attribute dataset [1,11]. The PCA reduces dimensions based on the variance additivity of irrelevant principal components [21,22], which can be evaluated by the contribution rate [19,27].…”

Section: Literature Review 21 Index System Reduction Algorithmsmentioning

confidence: 99%

Index System Reduction Method Based on the Index Similarity

Gao

2019

Teh. vjesn.

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Multi-attribute decision making (MADM) always suffers from the result inconsistency and computational complexity problem, due to numbers of redundant and relational attributes (indexes) of the initial evaluation index system. Therefore, this paper studies the index system (IS) reduction problem through selecting the most representative indicator from each index subsystem after the IS structure partition. First, we propose and demonstrate the Index Subsystem Judgement theorem to improve the efficiency of the classic system structure partition algorithm. Second, an algorithm of index system reduction based on the index similarity (ISRS) is put forward. The ISRS is able to reduce the index quantity while still keeping the index meaning. Third, we define the direction loss rate to measure the evaluation ability loss of the IS during reduction. The algorithm is tested for a synthetic dataset to compare the proposed ISRS with different index reduction algorithms, followed by an extensive experimentation with a real-world financial dataset. Experiment results illustrate that our proposed method is able to obtain more accessible and available reduction results in practice.

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Section: Literature Review 21 Index System Reduction Algorithmsmentioning

confidence: 99%

Index System Reduction Method Based on the Index Similarity

Gao

2019

Teh. vjesn.

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“…Classical rough sets mainly deal with discrete data, while information tables in the real world can be quite complicated, and many value measurements are given as continuous data, such as temperature, time and voltage. Classical rough sets cannot be used for approximation relation representation of interval data in uncertain systems (Hu, 2016; Liang, 2015; Gu, 2015; Bi et al , 2016). The grey system (Deng, 1988) is an efficient tool for interval data processing (Deng, 1988; Liu, 2006; Nagai and Yamaguchi, 2004), hence, grey rough sets provide a new tool for knowledge acquisition from uncertain information systems, especially for the interval-valued information system (Yao, 1993, 1996; Yeung and Chen, 2005).…”

Section: Introductionmentioning

confidence: 99%

Intelligent reasoning and management decision making with grey rough influence diagrams

Liu

Shen

2016

IJICC

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Purpose Influence diagrams (IDs) have been widely applied as a form of knowledge expression and a decision analysis tool in the management and engineering fields. Relationship measurements and expectation values are computed depending on probability distributions in traditional IDs, however, most information systems in the real world are nondeterministic, and data in information tables can be interval valued, multiple valued and even incomplete. Consequently, conventional numeric models of IDs are not suitable for information processing with respect to imprecise data whose boundaries are uncertain. The paper aims to discuss these issues. Design/methodology/approach The grey system theory and rough sets have proved to be effective tools in the data processing of uncertain information systems, approximate knowledge acquisition and representation are also the objectives in intelligent reasoning and decision analysis. Hence, this study proposes a new mathematical model by combining grey rough sets with IDs, and approximate measurements are used instead of probability distribution, an implicational relationship is utilized instead of an indiscernible relationship, and all of the features of the proposed approach contribute to deal with uncertain problems. Findings The focus of this paper is to provide a more comprehensive framework for approximate knowledge representation and intelligent decision analysis in uncertain information systems and an example of decision support in product management systems with the new approach is illustrated. Originality/value Collaboration of IDs and grey rough sets is first proposed, which provides a new mathematical and graphical tool for approximate reasoning and intelligent decision analysis within interval-valued information systems.

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“…X. Wei then present a novel search pattern named ExNa by defining its model and basic operations in detail. Two attribute reduction methods based on minimum decision cost are proposed by Z. Bi from the algebraic view and the information theory, respectively. Y. Wang designs an optimization algorithm to solve the problem.…”

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confidence: 99%

Big data‐related technologies and applications

Luo

Liu

2016

Concurrency and Computation

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With the advances of information communication technologies, it is critical to improve the efficiency and accuracy of modern data processing techniques. The past decade has witnessed the tremendous technical advances in sensor networks, Internet/Web of Things, cloud computing, mobile/embedded computing, spatial/temporal data processing, and big data, and these technologies have provided new opportunities and solutions to data processing techniques. Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions, and web logs and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision-making (Veracity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make business more agile, and to answer questions that were previously considered beyond our reach. This special issue wants to demonstrate the emerging issues in the research of big data and approaches towards it. Original and research articles are solicited in all aspects including theoretical studies, practical applications, and experimental prototypes.The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. Totally, we have received 20 manuscripts and 11 papers are accepted. Five papers are selected from SKG2015 conference with about 50% new content. This special issue will be published by Concurrency and Computation: Practice and Experience as special issues.In order to detect and describe the real-time urban emergency event, the knowledge base model is proposed by the paper by Z. Xu et al [1]. The crowdsourcing-based knowledge base model is introduced, which uses the information from social media. X. Lin et al [2]. introduced one such comprehensive schemes: it takes consideration of the DG's own characteristics and its ability to support the local loading, it adopts different protection strategy, and realizes the fault isolation and island division through the coordination between the protection and the automatic devices. J. Zheng [3] extracted the feature information and also mined the association rules to identify and mark the unknown protocol using the learning mechanism of machine. The unknown protocol in a specific environment was found and analyzed by marking t...

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Attribute reduction in decision‐theoretic rough set model based on minimum decision cost

Cited by 9 publications

References 51 publications

Index System Reduction Method Based on the Index Similarity

Index System Reduction Method Based on the Index Similarity

Intelligent reasoning and management decision making with grey rough influence diagrams

Big data‐related technologies and applications

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