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Multi-granularity dominance rough concept attribute reduction over hybrid information systems and its application in clinical decision-making. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.03.048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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2
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Liang M, Mi J, Feng T, Jin C. Attribute reduction in intuitionistic fuzzy formal concepts. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-202719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Knowledge acquisition in intuitionistic fuzzy information systems is of importance because those fuzzy information systems are often encountered in many real-life problems. Formal concept analysis is a simple and effective tool for knowledge acquisition. However, there is still little work on introducing knowledge acquisition methods based on formal concept analysis into intuitionistic fuzzy information systems. This paper mainly extends the formal concept theory into intuitionistic fuzzy information systems. Firstly, two pairs of adjoint mappings are defined in intuitionistic fuzzy formal contexts. It is verified that both pairs of adjoint mappings form Galois connections. Secondly, two types of intuitionistic fuzzy concept lattices are constructed. After that, we also present the main theorems and propositions of the intuitionistic fuzzy concept lattices. Thirdly, we deeply discuss the attribute characteristics for type-1 generalized one-sided intuitionistic fuzzy concept lattice. Furthermore, a discernibility matrix-based algorithm is proposed for attribute reduction and the effectiveness of this algorithm is demonstrated by a practical example. The construction of intuitionistic fuzzy conceptS is meaningful for the complex and fuzzy information in real life.
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Affiliation(s)
- Meishe Liang
- Department of Mathematics and Physics, Shijiazhuang Tiedao University, Shijiazhuang, P.R. China
- College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, P.R. China
- Hebei Key Laboratory of Computational Mathematicsand Applications, Shijiazhuang, P.R. China
| | - Jusheng Mi
- College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, P.R. China
- Hebei Key Laboratory of Computational Mathematicsand Applications, Shijiazhuang, P.R. China
| | - Tao Feng
- College of Science, Hebei University of Scienceand Technology, Shijiazhuang, P.R. China
| | - Chenxia Jin
- College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, P.R. China
- School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, P.R. China
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Sangeetha T, Geetha Mary A. Rough set-based entropy measure with weighted density outlier detection method. OPEN COMPUTER SCIENCE 2022. [DOI: 10.1515/comp-2020-0228] [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] Open
Abstract
Abstract
The rough set theory is a powerful numerical model used to handle the impreciseness and ambiguity of data. Many existing multigranulation rough set models were derived from the multigranulation decision-theoretic rough set framework. The multigranulation rough set theory is very desirable in many practical applications such as high-dimensional knowledge discovery, distributional information systems, and multisource data processing. So far research works were carried out only for multigranulation rough sets in extraction, selection of features, reduction of data, decision rules, and pattern extraction. The proposed approach mainly focuses on anomaly detection in qualitative data with multiple granules. The approximations of the dataset will be derived through multiequivalence relation, and then, the rough set-based entropy measure with weighted density method is applied on every object and attribute. For detecting outliers, threshold value fixation is performed based on the estimated weight. The performance of the algorithm is evaluated and compared with existing outlier detection algorithms. Datasets such as breast cancer, chess, and car evaluation have been taken from the UCI repository to prove its efficiency and performance.
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Affiliation(s)
- Tamilarasu Sangeetha
- School of Computer Science and Engineering, Vellore Institute of Technology , Vellore 632 001 , Tamil Nadu , India
| | - Amalanathan Geetha Mary
- School of Computer Science and Engineering, Vellore Institute of Technology , Vellore 632 001 , Tamil Nadu , India
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Zhang X, Li J, Mi J. Dynamic updating approximations approach to multi-granulation interval-valued hesitant fuzzy information systems with time-evolving attributes. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107809] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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6
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Multigranulation double-quantitative decision-theoretic rough sets based on logical operations. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-021-01476-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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7
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Kong Q, Xu W, Zhang D. A comparative study of different granular structures induced from the information systems. Soft comput 2022. [DOI: 10.1007/s00500-021-06499-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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8
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Double-quantitative multigranulation rough fuzzy set based on logical operations in multi-source decision systems. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01433-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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9
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Multigranulation Roughness of Intuitionistic Fuzzy Sets by Soft Relations and Their Applications in Decision Making. MATHEMATICS 2021. [DOI: 10.3390/math9202587] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Multigranulation rough set (MGRS) based on soft relations is a very useful technique to describe the objectives of problem solving. This MGRS over two universes provides the combination of multiple granulation knowledge in a multigranulation space. This paper extends the concept of fuzzy set Shabir and Jamal in terms of an intuitionistic fuzzy set (IFS) based on multi-soft binary relations. This paper presents the multigranulation roughness of an IFS based on two soft relations over two universes with respect to the aftersets and foresets. As a result, two sets of IF soft sets with respect to the aftersets and foresets are obtained. These resulting sets are called lower approximations and upper approximations with respect to the aftersets and with respect to the foresets. Some properties of this model are studied. In a similar way, we approximate an IFS based on multi-soft relations and discuss their some algebraic properties. Finally, a decision-making algorithm has been presented with a suitable example.
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Chen Y, Wang P, Yang X, Mi J, Liu D. Granular ball guided selector for attribute reduction. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107326] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Li M, Zhang C, Chen M, Xu W. On local multigranulation covering decision-theoretic rough sets. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-202274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Multi-granulation decision-theoretic rough sets uses the granular structures induced by multiple binary relations to approximate the target concept, which can get a more accurate description of the approximate space. However, Multi-granulation decision-theoretic rough sets is very time-consuming to calculate the approximate value of the target set. Local rough sets not only inherits the advantages of classical rough set in dealing with imprecise, fuzzy and uncertain data, but also breaks through the limitation that classical rough set needs a lot of labeled data. In this paper, in order to make full use of the advantage of computational efficiency of local rough sets and the ability of more accurate approximation space description of multi-granulation decision-theoretic rough sets, we propose to combine the local rough sets and the multigranulation decision-theoretic rough sets in the covering approximation space to obtain the local multigranulation covering decision-theoretic rough sets model. This provides an effective tool for discovering knowledge and making decisions in relation to large data sets. We first propose four types of local multigranulation covering decision-theoretic rough sets models in covering approximation space, where a target concept is approximated by employing the maximal or minimal descriptors of objects. Moreover, some important properties and decision rules are studied. Meanwhile, we explore the reduction among the four types of models. Furthermore, we discuss the relationships of the proposed models and other representative models. Finally, illustrative case of medical diagnosis is given to explain and evaluate the advantage of local multigranulation covering decision-theoretic rough sets model.
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Affiliation(s)
- Mengmeng Li
- School of Mathematics, Harbin Institute of Technology, Harbin, P.R. China
| | - Chiping Zhang
- School of Mathematics, Harbin Institute of Technology, Harbin, P.R. China
| | - Minghao Chen
- School of Mathematical Sciences, Dalian University ofTechnology, Dalian, P.R. China
| | - Weihua Xu
- College of Artificial Intelligence, Southwest University, Chongqing, P.R. China
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Jiang C, Guo D, Xu R. Measuring the outcome of movement-based three-way decision using proportional utility functions. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02325-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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17
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Hu C, Zhang L, Liu S. Incremental approaches to update multigranulation approximations for dynamic information systems. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Multigranulation rough set (MGRS) theory provides an effective manner for the problem solving by making use of multiple equivalence relations. As the information systems always dynamically change over time due to the addition or deletion of multiple objects, how to efficiently update the approximations in multigranulation spaces by making fully utilize the previous results becomes a crucial challenge. Incremental learning provides an efficient manner because of the incorporation of both the current information and previously obtained knowledge. In spite of the success of incremental learning, well-studied findings performed to update approximations in multigranulation spaces have relatively been scarce. To address this issue, in this paper, we propose matrix-based incremental approaches for updating approximations from the perspective of multigranulation when multiple objects vary over time. Based on the matrix characterization of multigranulation approximations, the incremental mechanisms for relevant matrices are systematically investigated while adding or deleting multiple objects. Subsequently, in accordance with the incremental mechanisms, the corresponding incremental algorithms for maintaining multigranulation approximations are developed to reduce the redundant computations. Finally, extensive experiments on eight datasets available from the University of California at Irvine (UCI) are conducted to verify the effectiveness and efficiency of the proposed incremental algorithms in comparison with the existing non-incremental algorithm.
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Affiliation(s)
- Chengxiang Hu
- School of Computer and Information Engineering, Chuzhou University, Chuzhou, Anhui, China
- School of Computer Science and Technology, Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, Jiangsu, China
| | - Li Zhang
- School of Computer Science and Technology, Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, Jiangsu, China
- Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, Jiangsu, China
| | - Shixi Liu
- School of Computer and Information Engineering, Chuzhou University, Chuzhou, Anhui, China
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Xin XW, Song JH, Xue ZA, Peng WM. Intuitionistic fuzzy three-way formal concept analysis based attribute correlation degree. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-200002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
As an important expanded of the classical formal concept, the three-way formal concept analysis integrates more information with the three-way decision theory. However, to the best of our knowledge, few scholars have studied the intuitionistic fuzzy three-way formal concept analysis. This paper proposes an intuitionistic fuzzy three-way formal concept analysis model based on the attribute correlation degree. To achieve this, we comprehensively analyze the composition of attribute correlation degree in the intuitionistic fuzzy environment, and introduce the corresponding calculation methods for different situations, as well as prove the related properties. Furthermore, we investigate the intuitionistic fuzzy three-way concept lattice ((IF3WCL) of object-induced and attribute-induced. Then, the relationship between the IF3WCL and the positive, negative and boundary domains in the three-way decision are discussed. In addition, considering the final decision problem of boundary objects, the secondary decision strategy of boundary objects is obtained for IF3WCL. Finally, a numerical example of multinational company investment illustrates the effectiveness of the proposed model. In this paper, we systematically study the IF3WCL, and give a quantitative analysis method of formal concept decision along with its connection with three-way decision, which provides new ideas for the related research.
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Affiliation(s)
- Xian-Wei Xin
- College of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Ji-Hua Song
- College of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhan-Ao Xue
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Wei-Ming Peng
- College of Artificial Intelligence, Beijing Normal University, Beijing, China
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Yang J, Zhou W, Li S. Similarity measure for multi-granularity rough approximations of vague sets. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-200611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Vague sets are a further extension of fuzzy sets. In rough set theory, target concept can be characterized by different rough approximation spaces when it is a vague concept. The uncertainty measure of vague sets in rough approximation spaces is an important issue. If the uncertainty measure is not accurate enough, different rough approximation spaces of a vague concept may possess the same result, which makes it impossible to distinguish these approximation spaces for charactering a vague concept strictly. In this paper, this problem will be solved from the perspective of similarity. Firstly, based on the similarity between vague information granules(VIGs), we proposed an uncertainty measure with strong distinguishing ability called rough vague similarity (RVS). Furthermore, by studying the multi-granularity rough approximations of a vague concept, we reveal the change rules of RVS with the changing granularities and conclude that the RVS between any two rough approximation spaces can degenerate to granularity measure and information measure. Finally, a case study and related experiments are listed to verify that RVS possesses a better performance for reflecting differences among rough approximation spaces for describing a vague concept.
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Affiliation(s)
- Jie Yang
- National Pilot School of Software, Yunnan University, Kunming, China
- School of Physics and Electronic Science, Zunyi Normal University, Zunyi, China
| | - Wei Zhou
- National Pilot School of Software, Yunnan University, Kunming, China
| | - Shuai Li
- School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang, China
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20
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Bao H, Wu WZ, Zheng JW, Li TJ. Entropy based optimal scale combination selection for generalized multi-scale information tables. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-020-01243-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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21
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Jiang Z, Liu K, Song J, Yang X, Li J, Qian Y. Accelerator for crosswise computing reduct. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106740] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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22
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Hu C, Zhang L. Dynamic dominance-based multigranulation rough sets approaches with evolving ordered data. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-020-01119-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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23
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A multigranulation fuzzy rough approach to multisource information systems. Soft comput 2021. [DOI: 10.1007/s00500-020-05187-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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24
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Hu C, Zhang L. Efficient approaches for maintaining dominance-based multigranulation approximations with incremental granular structures. Int J Approx Reason 2020. [DOI: 10.1016/j.ijar.2020.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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25
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Xu Y, Wang Q, Sun W. Matrix-based incremental updating approximations in multigranulation rough set under two-dimensional variation. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01219-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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26
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27
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Jiang Z, Dou H, Song J, Wang P, Yang X, Qian Y. Data-guided multi-granularity selector for attribute reduction. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01846-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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30
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Rao X, Liu K, Song J, Yang X, Qian Y. Gaussian kernel fuzzy rough based attribute reduction: An acceleration approach. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Xiansheng Rao
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Keyu Liu
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Jingjing Song
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
- Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, China
| | - Xibei Yang
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Yuhua Qian
- Institute of Big Data Science and Industry, Shanxi University, Taiyuan, Shanxi, China
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31
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Rao X, Yang X, Yang X, Chen X, Liu D, Qian Y. Quickly calculating reduct: An attribute relationship based approach. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106014] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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32
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Multi-granulation method for information fusion in multi-source decision information system. Int J Approx Reason 2020. [DOI: 10.1016/j.ijar.2020.04.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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33
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Zou L, Pang K, Song X, Kang N, Liu X. A knowledge reduction approach for linguistic concept formal context. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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34
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35
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Construction of three-way attribute partial order structure via cognitive science and granular computing. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105859] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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36
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37
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38
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39
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Tan A, Wu WZ, Li J, Li T. Reduction foundation with multigranulation rough sets using discernibility. Artif Intell Rev 2020. [DOI: 10.1007/s10462-019-09737-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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40
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Jiang Z, Liu K, Yang X, Yu H, Fujita H, Qian Y. Accelerator for supervised neighborhood based attribute reduction. Int J Approx Reason 2020. [DOI: 10.1016/j.ijar.2019.12.013] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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41
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Liu K, Yang X, Yu H, Fujita H, Chen X, Liu D. Supervised information granulation strategy for attribute reduction. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01107-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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42
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Shao MW, Wu WZ, Wang XZ, Wang CZ. Knowledge reduction methods of covering approximate spaces based on concept lattice. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105269] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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43
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Fan A, Wang H, Xiang H, Zou X. Inferring Large-Scale Gene Regulatory Networks Using a Randomized Algorithm Based on Singular Value Decomposition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1997-2008. [PMID: 29993839 DOI: 10.1109/tcbb.2018.2825446] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Reconstructing large-scale gene regulatory networks (GRNs) is a challenging problem in the field of computational biology. Various methods for inferring GRNs have been developed, but they fail to accurately infer GRNs with a large number of genes. Additionally, the existing evaluation indexes for evaluating the constructed networks have obvious disadvantages because GRNs in most biological systems are sparse. In this paper, we develop a new method for inferring GRNs based on randomized singular value decomposition (RSVD) and ordinary differential equation (ODE)-based optimization, denoted as IGRSVD, from large-scale time series data with noise. The three major contributions of this paper are as follows. First, the IGRSVD algorithm uses the RSVD to handle the noise and reduce the original large-scale data into small-scale problems. Second, we propose two new evaluated indexes, the expected value accuracy (EVA) and the expected value error (EVE), to evaluate the performance of inferred networks by considering the sparse features in the network. Finally, the proposed IGRSVD algorithm is compared with the existing SVD algorithm and PCA_CMI algorithm using four subsets from E. coli and datasets from DREAM challenge. The experimental results demonstrate that the IGRSVD algorithm is effective and more suitable for reconstructing large-scale networks.
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Lin Y, Li J, Tan A, Zhang J. Granular matrix-based knowledge reductions of formal fuzzy contexts. INT J MACH LEARN CYB 2019. [DOI: 10.1007/s13042-019-01022-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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46
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A knowledge acquisition method based on concept lattice and inclusion degree for ordered information systems. INT J MACH LEARN CYB 2019. [DOI: 10.1007/s13042-019-01014-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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47
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Shao MW, Wu WZ, Wang CZ. Axiomatic characterizations of adjoint generalized (dual) concept systems. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-182612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Ming-Wen Shao
- College of Computer & Communication Engineering, China University of Petroleum, Qingdao, Shandong, P. R. China
| | - Wei-Zhi Wu
- School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang, P. R. China
| | - Chang-Zhong Wang
- Department of Mathematics, Bohai University, Jinzhou, P. R. China
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48
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She Y, He X, Qian T, Wang Q, Zeng W. A theoretical study on object-oriented and property-oriented multi-scale formal concept analysis. INT J MACH LEARN CYB 2019. [DOI: 10.1007/s13042-019-01015-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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49
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Yu P, Wang H, Li J, Lin G. Matrix-based approaches for updating approximations in neighborhood multigranulation rough sets while neighborhood classes decreasing or increasing. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-190034] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Peiqiu Yu
- School of Mathematics and Statistics, Minnan normal university, Fujian, Zhangzhou, China
| | - Hongkun Wang
- Georgetown University, Georgetown, Washington DC, USA
| | - Jinjin Li
- School of Mathematics and Statistics, Minnan normal university, Fujian, Zhangzhou, China
| | - Guoping Lin
- School of Mathematics and Statistics, Minnan normal university, Fujian, Zhangzhou, China
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50
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