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Zhang X, Chen X, Xu W, Ding W. Dynamic information fusion in multi-source incomplete interval-valued information system with variation of information sources and attributes. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.06.054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Liang D, Fu Y, Xu Z. Novel AQM analysis approach based on similarity and dissimilarity measures of interval set for multi-expert multi-criterion decision making. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2021.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Xie X, Zhang X, Zhang S. Rough set theory and attribute reduction in interval-set information system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-210662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
As an extension of traditional information systems, interval-set information systems have a strong expressive ability to describe uncertain information. Study of the rough set theory and the attribute reduction of interval-set information system are worth discussing. Here, the granularity structure of similar equivalence classes in an interval-set information system is mined, and an attribute reduction algorithm is constructed. The upper and lower approximation operators in the interval-set information system are defined. The accuracy and roughness are determined by these operators. At the same time, using rough sets, a concept of three branches of rough sets on the interval-set information system is constructed. The concepts of attribute dependency and attribute importance are induced by the positive number domain of the three branch domains, and they then lead to the attribute reduction algorithm. Experiments on the UCI datasets show that the uncertainty measure proposed in this paper is sensitive to the attributes and can effectively reduce redundant information of the interval-set information system.
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Affiliation(s)
- Xin Xie
- School of Mathematical Sciences, Sichuan Normal University, Chengdu, China
- Institute of Intelligent Information and Quantum Information, Sichuan Normal University, Chengdu, China
| | - Xianyong Zhang
- School of Mathematical Sciences, Sichuan Normal University, Chengdu, China
- Institute of Intelligent Information and Quantum Information, Sichuan Normal University, Chengdu, China
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Zhang Y, Jia X, Tang Z. Information-theoretic measures of uncertainty for interval-set decision tables. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.06.092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Jiang C, Guo D, Sun L. Effectiveness measure for TAO model of three-way decisions with interval set. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-202207] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The basic idea of the three-way decisions (3WD) is ‘thinking in threes.’ The TAO (trisecting-acting-outcome) model of 3WD includes three components, trisect a whole into three reasonable regions, devise a corresponding strategy on the trisection, and measure the effectiveness of the outcome. By reviewing existing studies, we found that only a few papers touch upon the third component, i.e., measure the effect. This paper’s principal aim is to present an effectiveness measure framework consisting of three parts: a specific TAO model - Change-based TAO model, interval sets, and utility functions with unique characteristics. Specifically, the change-based TAO model provides a method to measure effectiveness based on the difference before and after applying a strategy or an action. First, we use interval sets to represent these changes when a strategy or an action is applied. These changes correspond to three different intervals. Second, we use the utility measurement method to figure out three change intervals. Namely, different utility measures correspond to the different intervals, concave utility metric, direct utility metric, and convex utility metric, respectively. Third, it aggregates the toll utility through the joint of the three utilities mentioned above. The weights among these three are adjusted by a dual expected utility function that conveys the decision-makers’ preferences. We give an example and experiment highlighting the validity and practicability of the utility measure method in the change-based TAO model of three-way decisions.
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Affiliation(s)
- Chunmao Jiang
- College of Computer Science and Information Engineer, Harbin Normal University, Harbin, Heilongjiang Province, China
| | - Doudou Guo
- College of Computer Science and Information Engineer, Harbin Normal University, Harbin, Heilongjiang Province, China
| | - Lijuan Sun
- College of Computer Science and Information Engineer, Harbin Normal University, Harbin, Heilongjiang Province, China
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Fernandes M, Vieira SM, Leite F, Palos C, Finkelstein S, Sousa JM. Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review. Artif Intell Med 2020; 102:101762. [DOI: 10.1016/j.artmed.2019.101762] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 08/29/2019] [Accepted: 11/07/2019] [Indexed: 12/23/2022]
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Uncertainty measures for interval set information tables based on interval δ-similarity relation. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.06.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Yu J, Li Y, Chen M, Zhang B, Xu W. Decision-theoretic rough set in lattice-valued decision information system1. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-172111] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jianhang Yu
- Department of Mathematics, Harbin Institute of Technology, Harbin, P.R. China
- Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, Suita, Japan
| | - Yingqin Li
- College of Jiamusi, Heilongjiang University of Chinese Medicine, Jiamusi, Heilongjiang, P.R. China
| | - Minghao Chen
- Department of Mathematics, Harbin Institute of Technology, Harbin, P.R. China
| | - Biao Zhang
- Department of Mathematics, Harbin Institute of Technology, Harbin, P.R. China
| | - Weihua Xu
- School of Mathematics and Statistics, Chongqing University of Technology, Chongqing, P.R. China
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Wang P, Zhang P, Li Z. A three-way decision method based on Gaussian kernel in a hybrid information system with images: An application in medical diagnosis. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.01.031] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Yu J, Zhang B, Chen M, Xu W. Double-quantitative decision-theoretic approach to multigranulation approximate space. Int J Approx Reason 2018. [DOI: 10.1016/j.ijar.2018.05.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Liang D, Liu D, Quan W. Information Aggregation of Hesitant Fuzzy Interval Sets for Multicriteria Decision-Making. Comput Sci Eng 2018. [DOI: 10.1109/mcse.2018.108163444] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Decui Liang
- University of Electronic Science and Technology of China
| | - Dun Liu
- Southwest Jiaotong University
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Hu BQ, Wong H, Yiu KFC. Equivalent Structures of Interval Sets and Fuzzy Interval Sets. INT J INTELL SYST 2017. [DOI: 10.1002/int.21940] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Bao Qing Hu
- School of Mathematics and Statistics; Wuhan University; Wuhan 430072 People's Republic of China
- Computational Science Hubei Key Laboratory; Wuhan University; Wuhan 430072 People's Republic of China
| | - Heung Wong
- Department of Applied Mathematics; The Hong Kong Polytechnic University; Hong Kong People's Republic of China
| | - Ka-fai Cedric Yiu
- Department of Applied Mathematics; The Hong Kong Polytechnic University; Hong Kong People's Republic of China
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Wu WZ, Qian Y, Li TJ, Gu SM. On rule acquisition in incomplete multi-scale decision tables. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.03.041] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Singh SK, Rastogi V, Singh SK. Pain Assessment Using Intelligent Computing Systems. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES INDIA SECTION A-PHYSICAL SCIENCES 2016. [DOI: 10.1007/s40010-015-0260-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Zhang HY, Yang SY, Ma JM. Ranking interval sets based on inclusion measures and applications to three-way decisions. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2015.07.025] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Yu J, Xu W. Incremental knowledge discovering in interval-valued decision information system with the dynamic data. INT J MACH LEARN CYB 2015. [DOI: 10.1007/s13042-015-0473-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Liu D, Li T, Zhang J. Incremental updating approximations in probabilistic rough sets under the variation of attributes. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2014.09.008] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Zhang X, Mei C, Chen D, Li J. Multi-confidence rule acquisition and confidence-preserved attribute reduction in interval-valued decision systems. Int J Approx Reason 2014. [DOI: 10.1016/j.ijar.2014.05.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Liu D, Li T, Zhang J. A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems. Int J Approx Reason 2014. [DOI: 10.1016/j.ijar.2014.05.009] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Clark PG, Grzymala-Busse JW, Rzasa W. Mining incomplete data with singleton, subset and concept probabilistic approximations. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.05.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Approximation set of the interval set in Pawlak's space. ScientificWorldJournal 2014; 2014:317387. [PMID: 25177721 PMCID: PMC4142747 DOI: 10.1155/2014/317387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Revised: 07/19/2014] [Accepted: 07/19/2014] [Indexed: 11/17/2022] Open
Abstract
The interval set is a special set, which describes uncertainty of an uncertain concept or set Z with its two crisp boundaries named upper-bound set and lower-bound set. In this paper, the concept of similarity degree between two interval sets is defined at first, and then the similarity degrees between an interval set and its two approximations (i.e., upper approximation set R¯(Z) and lower approximation set R_(Z)) are presented, respectively. The disadvantages of using upper-approximation set R¯(Z) or lower-approximation set R_(Z) as approximation sets of the uncertain set (uncertain concept) Z are analyzed, and a new method for looking for a better approximation set of the interval set Z is proposed. The conclusion that the approximation set R0.5(Z) is an optimal approximation set of interval set Z is drawn and proved successfully. The change rules of R0.5(Z) with different binary relations are analyzed in detail. Finally, a kind of crisp approximation set of the interval set Z is constructed. We hope this research work will promote the development of both the interval set model and granular computing theory.
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Zhang J, Wong JS, Li T, Pan Y. A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems. Int J Approx Reason 2014. [DOI: 10.1016/j.ijar.2013.08.003] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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She Y. On the rough consistency measures of logic theories and approximate reasoning in rough logic. Int J Approx Reason 2014. [DOI: 10.1016/j.ijar.2013.10.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Pombo N, Araújo P, Viana J. Knowledge discovery in clinical decision support systems for pain management: a systematic review. Artif Intell Med 2013; 60:1-11. [PMID: 24370382 DOI: 10.1016/j.artmed.2013.11.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 11/18/2013] [Accepted: 11/29/2013] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The occurrence of pain accounts for billions of dollars in annual medical expenditures; loss of quality of life and decreased worker productivity contribute to indirect costs. As pain is highly subjective, clinical decision support systems (CDSSs) can be critical for improving the accuracy of pain assessment and offering better support for clinical decision-making. This review is focused on computer technologies for pain management that allow CDSSs to obtain knowledge from the clinical data produced by either patients or health care professionals. METHODS AND MATERIALS A comprehensive literature search was conducted in several electronic databases to identify relevant articles focused on computerised systems that constituted CDSSs and include data or results related to pain symptoms from patients with acute or chronic pain, published between 1992 and 2011 in the English language. In total, thirty-nine studies were analysed; thirty-two were selected from 1245 citations, and seven were obtained from reference tracking. RESULTS The results highlighted the following clusters of computer technologies: rule-based algorithms, artificial neural networks, nonstandard set theory, and statistical learning algorithms. In addition, several methodologies were found for content processing such as terminologies, questionnaires, and scores. The median accuracy ranged from 53% to 87.5%. CONCLUSIONS Computer technologies that have been applied in CDSSs are important but not determinant in improving the systems' accuracy and the clinical practice, as evidenced by the moderate correlation among the studies. However, these systems play an important role in the design of computerised systems oriented to a patient's symptoms as is required for pain management. Several limitations related to CDSSs were observed: the lack of integration with mobile devices, the reduced use of web-based interfaces, and scarce capabilities for data to be inserted by patients.
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Affiliation(s)
- Nuno Pombo
- Department of Informatics, University of Beira Interior, Rua Marquês de Ávila e Bolama, 6201-001 Covilhã, Portugal.
| | - Pedro Araújo
- Instituto de Telecomunicações and Department of Informatics, University of Beira Interior, Rua Marquês de Ávila e Bolama, 6201-001 Covilhã, Portugal
| | - Joaquim Viana
- Faculty of Health Sciences, University of Beira Interior, Av. Infante D. Henrique, 6200-506 Covilhã, Portugal
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Li J, Mei C, Lv Y. Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reason 2013. [DOI: 10.1016/j.ijar.2012.07.005] [Citation(s) in RCA: 206] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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