{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T05:31:10Z","timestamp":1773725470066,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T00:00:00Z","timestamp":1654905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T00:00:00Z","timestamp":1654905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976082"],"award-info":[{"award-number":["61976082"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002103"],"award-info":[{"award-number":["62002103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10489-022-03760-5","type":"journal-article","created":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T18:02:39Z","timestamp":1654970559000},"page":"4524-4540","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Feature selection using self-information uncertainty measures in neighborhood information systems"],"prefix":"10.1007","volume":"53","author":[{"given":"Jiucheng","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5062-5012","authenticated-orcid":false,"given":"Kanglin","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Yuanhao","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"issue":"8","key":"3760_CR1","doi-asserted-by":"publisher","first-page":"2488","DOI":"10.1007\/s10489-020-01637-z","volume":"50","author":"EL Lin","year":"2020","unstructured":"Lin EL, Chen Q, Qi XM (2020) Deep reinforcement learning for imbalanced classification. Appl Intell 50(8):2488\u20132502","journal-title":"Appl Intell"},{"issue":"3","key":"3760_CR2","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1007\/s10489-020-01863-5","volume":"51","author":"SX Bai","year":"2021","unstructured":"Bai SX, Lin YJ, Lv Y, Chen JK, Wang CX (2021) Kernelized fuzzy rough sets based online streaming feature selection for large-scale hierarchical classification. Appl Intell 51(3):1602\u20131615","journal-title":"Appl Intell"},{"key":"3760_CR3","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.patcog.2019.02.016","volume":"91","author":"S Sharmin","year":"2019","unstructured":"Sharmin S, Shoyaib M, Ali AA (2019) Simultaneous feature selection and discretization based on mutual information. Pattern Recognit 91:162\u2013174","journal-title":"Pattern Recognit"},{"key":"3760_CR4","doi-asserted-by":"publisher","unstructured":"Bugata P, Drotar P (2020) On some aspects of minimum redundancy maximum relevance feature selection. Sci China Inf Sci. https:\/\/doi.org\/10.1007\/s11432-019-2633-y","DOI":"10.1007\/s11432-019-2633-y"},{"key":"3760_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.patrec.2018.06.005","volume":"112","author":"WF Gao","year":"2018","unstructured":"Gao WF, Hu L, Zhang P, He JL (2018) Feature selection considering the composition of feature relevancy. Pattern Recognit Letters 112:70\u201374","journal-title":"Pattern Recognit Letters"},{"key":"3760_CR6","doi-asserted-by":"publisher","unstructured":"Wei GF, Zhao J, Feng YL, He AX, Yu J (2020) A novel hybrid feature selection method based on dynamic feature importance. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2020.106337","DOI":"10.1016\/j.asoc.2020.106337"},{"issue":"12","key":"3760_CR7","doi-asserted-by":"publisher","first-page":"4615","DOI":"10.1007\/s10489-018-1239-6","volume":"48","author":"P Zhang","year":"2018","unstructured":"Zhang P, Gao WF, Liu GX (2018) Feature selection considering weighted relevancy. Appl Intell 48(12):4615\u20134625","journal-title":"Appl Intell"},{"key":"3760_CR8","doi-asserted-by":"publisher","unstructured":"Xu JC, Qu KL, Yang Y (2021) Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System. Entropy. https:\/\/doi.org\/10.3390\/e23060704","DOI":"10.3390\/e23060704"},{"key":"3760_CR9","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/j.ins.2019.07.051","volume":"505","author":"KY Liu","year":"2019","unstructured":"Liu KY, Yang XB, Fujita H, Liu D, Yang X, Qian YH (2019) An efficient selector for multi-granularity attribute reduction. Inf Sci 505:457\u2013472","journal-title":"Inf Sci"},{"key":"3760_CR10","doi-asserted-by":"publisher","unstructured":"Li JD, Cheng KW, Wang SH, Morstatter F, Trevino RP, Tang JL, Liu H (2018) Feature Selection: A Data Perspective. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3136625","DOI":"10.1145\/3136625"},{"issue":"3","key":"3760_CR11","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s10489-019-01543-z","volume":"50","author":"SA Shahee","year":"2020","unstructured":"Shahee SA, Ananthakumar U (2020) An effective distance based feature selection approach for imbalanced data. Appl Intell 50(3):717\u2013745","journal-title":"Appl Intell"},{"issue":"1","key":"3760_CR12","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ins.2006.06.006","volume":"177","author":"Z Pawlak","year":"2007","unstructured":"Pawlak Z, Skowron A (2007) Rough sets: Some extensions. Inf Sci 177(1):28\u201340","journal-title":"Inf Sci"},{"key":"3760_CR13","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.ins.2020.05.060","volume":"538","author":"X Yang","year":"2020","unstructured":"Yang X, Li TR, Liu D, Fujita H (2020) A multilevel neighborhood sequential decision approach of three-way granular computing. Inf Sci 538:119\u2013141","journal-title":"Inf Sci"},{"key":"3760_CR14","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.compind.2018.01.014","volume":"97","author":"TK Sheeja","year":"2018","unstructured":"Sheeja TK, Kuriakose A S (2018) A novel feature selection method using fuzzy rough sets. Comput Ind 97:111\u2013116","journal-title":"Comput Ind"},{"key":"3760_CR15","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.ins.2020.04.038","volume":"539","author":"P Ni","year":"2020","unstructured":"Ni P, Zhao SY, Wang XZ, Chen H, Li CP (2020) Incremental feature selection based on fuzzy rough sets. Inf Sci 539:185\u2013204","journal-title":"Inf Sci"},{"issue":"9","key":"3760_CR16","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1007\/s10489-020-01675-7","volume":"50","author":"YL Cai","year":"2020","unstructured":"Cai YL, Zhang HG, He Q, Duan J (2020) A novel framework of fuzzy oblique decision tree construction for pattern classification. Appl Intell 50(9):2959\u20132975","journal-title":"Appl Intell"},{"key":"3760_CR17","first-page":"296","volume":"27","author":"DQ Miao","year":"2001","unstructured":"Miao DQ (2001) Discretization of continuous attributes in rough set theory. Acta Autom Sin 27:296\u2013302","journal-title":"Acta Autom Sin"},{"key":"3760_CR18","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.ins.2018.07.065","volume":"507","author":"XD Yue","year":"2020","unstructured":"Yue XD, Chen YF, Miao DQ, Fujita H (2020) Fuzzy neighborhood covering for three-way classification. Inf Sci 507:795\u2013808","journal-title":"Inf Sci"},{"issue":"18","key":"3760_CR19","doi-asserted-by":"publisher","first-page":"3577","DOI":"10.1016\/j.ins.2008.05.024","volume":"178","author":"QH Hu","year":"2008","unstructured":"Hu Q H, Yu DR, Liu JF, Wu CX (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577\u20133594","journal-title":"Inf Sci"},{"key":"3760_CR20","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ins.2021.11.034","volume":"583","author":"KY Liu","year":"2021","unstructured":"Liu KY, Li TR, Yang XB, Yang X, Liu D, Zhang PF, Wang J (2021) Granular cabin: An efficient solution to neighborhood learning in big data. Inf Sci 583:189\u2013201","journal-title":"Inf Sci"},{"issue":"2","key":"3760_CR21","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s10489-019-01537-x","volume":"50","author":"KF Zheng","year":"2020","unstructured":"Zheng KF, Wang XJ, Wu B, Wu T (2020) Feature subset selection combining maximal information entropy and maximal information coefficient. Applied Intelligebce 50(2):487\u2013501","journal-title":"Applied Intelligebce"},{"issue":"1","key":"3760_CR22","doi-asserted-by":"publisher","first-page":"572","DOI":"10.2991\/ijcis.d.210106.003","volume":"14","author":"YY Chen","year":"2021","unstructured":"Chen YY, Chen YM (2021) Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets. Int J Comput Intell Syst 14(1):572\u2013581","journal-title":"Int J Comput Intell Syst"},{"key":"3760_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2016.02.013","volume":"56","author":"X Zhang","year":"2016","unstructured":"Zhang X, Mei CL, Chen DG, Liu JH (2016) Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy. Pattern Recognit 56:1\u201315","journal-title":"Pattern Recognit"},{"issue":"9","key":"3760_CR24","doi-asserted-by":"publisher","first-page":"4031","DOI":"10.1109\/TCYB.2019.2923430","volume":"50","author":"CZ Wang","year":"2020","unstructured":"Wang CZ, Huang Y, Shao MW, Hu QH, Chen DG (2020) Feature Selection Based on Neighborhood Self-Information. IEEE Trans Cybern 50(9):4031\u20134042","journal-title":"IEEE Trans Cybern"},{"key":"3760_CR25","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neucom.2015.06.010","volume":"168","author":"YJ Lin","year":"2015","unstructured":"Lin YJ, Hu QH, Liu JH, Duan J (2015) Multi-label feature selection based on max-dependency and min-redundancy. Neurocomputing 168:92\u2013103","journal-title":"Neurocomputing"},{"issue":"1","key":"3760_CR26","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/TFUZZ.2020.2989098","volume":"29","author":"L Sun","year":"2021","unstructured":"Sun L, Wang LY, Ding WP (2021) Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets. IEEE Trans Fuzzy Syst 29(1):19\u201333","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"10","key":"3760_CR27","doi-asserted-by":"publisher","first-page":"12392","DOI":"10.1016\/j.eswa.2009.04.057","volume":"36","author":"A Al-An","year":"2009","unstructured":"Al-An A (2009) A dependency-based search strategy for feature selection. Expert Syst Appl 36 (10):12392\u201312398","journal-title":"Expert Syst Appl"},{"key":"3760_CR28","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.ins.2020.11.021","volume":"549","author":"CZ Wang","year":"2021","unstructured":"Wang CZ, Huang Y, Ding WP, Cao ZH (2021) Attribute reduction with fuzzy rough self-information measures. Inf Sci 549:68\u201386","journal-title":"Inf Sci"},{"key":"3760_CR29","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.ijar.2019.12.013","volume":"119","author":"ZH Jiang","year":"2020","unstructured":"Jiang ZH, Liu KY, Yang XB, Yu HL, Fujita H, Qian YH (2020) Accelerator for supervised neighborhood based attribute reduction. Int J Approx Reason 119:122\u2013150","journal-title":"Int J Approx Reason"},{"key":"3760_CR30","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1016\/j.knosys.2018.10.010","volume":"163","author":"CX Hu","year":"2019","unstructured":"Hu CX, Zhang L, Wang BJ, Zhang Z, Li FZ (2019) Incremental updating knowledge in neighborhood multigranulation rough sets under dynamic granular structures. Knowl Based Syst 163:811\u2013829","journal-title":"Knowl Based Syst"},{"key":"3760_CR31","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1002\/int.10109","volume":"18","author":"GY Wang","year":"2003","unstructured":"Wang GY (2003) Rough Reduction in Algebra View and Information View. International Journal of Intelligent Systems 18:679\u2013688","journal-title":"International Journal of Intelligent Systems"},{"key":"3760_CR32","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.knosys.2018.10.038","volume":"164","author":"CZ Wang","year":"2019","unstructured":"Wang CZ, Huang Y, Shao MW, Fan XD (2019) Fuzzy rough set-based attribute reduction using distance measures. Knowl Based Syst 164:205\u2013212","journal-title":"Knowl Based Syst"},{"key":"3760_CR33","doi-asserted-by":"publisher","unstructured":"Jiang ZH, Liu KY, Song JJ, Yang XB, Li JH, Qian YH (2021) Accelerator for crosswise computing reduct. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2020.106740","DOI":"10.1016\/j.asoc.2020.106740"},{"key":"3760_CR34","first-page":"15","volume":"397","author":"J Fan","year":"2017","unstructured":"Fan J, Jiang YL, Liu Y (2017) Quick attribute reduction with generalized indiscernibility models. Inf Sci 397:15\u201336","journal-title":"Inf Sci"},{"key":"3760_CR35","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.knosys.2019.02.014","volume":"172","author":"MJ Cai","year":"2019","unstructured":"Cai MJ, Lang GM, Fujita H, Li ZY, Yang T (2019) Incremental approaches to updating reducts under dynamic covering granularity. Knowl Based Syst 172:130\u2013140","journal-title":"Knowl Based Syst"},{"issue":"1","key":"3760_CR36","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s10115-018-1185-y","volume":"58","author":"SM Saqlain","year":"2019","unstructured":"Saqlain SM, Sher M, Shah FA, Khan I (2019) Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowl Inf Syst 58 (1):139\u2013167","journal-title":"Knowl Inf Syst"},{"key":"3760_CR37","doi-asserted-by":"publisher","unstructured":"Yilmaz E (2013) An Expert System Based on Fisher Score and LS-SVM for Cardiac Arrhythmia Diagnosis. Comput Math Methods Med. https:\/\/doi.org\/10.1155\/2013\/849674","DOI":"10.1155\/2013\/849674"},{"key":"3760_CR38","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.ins.2019.05.072","volume":"502","author":"L Sun","year":"2019","unstructured":"Sun L, Zhang XY, Qian YH, Xu JC (2019) Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification. Inf Sci 502:18\u201341","journal-title":"Inf Sci"},{"issue":"4","key":"3760_CR39","first-page":"306","volume":"14","author":"CE Shannon","year":"1997","unstructured":"Shannon CE (1997) The mathematical theory of communication. MD Comput: Computers in Medical Practice 14(4):306\u2013317","journal-title":"MD Comput: Computers in Medical Practice"},{"key":"3760_CR40","doi-asserted-by":"publisher","unstructured":"Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2020) Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems. Knowl Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2019.105373","DOI":"10.1016\/j.knosys.2019.105373"},{"issue":"2","key":"3760_CR41","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/TFUZZ.2011.2173695","volume":"20","author":"DG Chen","year":"2012","unstructured":"Chen DG, Zhang L, Zhao SY, Hu QH, Zhu PF (2012) A novel algorithm for finding reducts with fuzzy rough sets. IEEE Trans Fuzzy Syst 20(2):385\u2013389","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1","key":"3760_CR42","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.fss.2014.04.029","volume":"258","author":"YH Qian","year":"2015","unstructured":"Qian YH, Wang Q, Cheng HH, Liang JY, Dang CY (2015) Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst 258(1):61\u201378","journal-title":"Fuzzy Sets Syst"},{"issue":"4","key":"3760_CR43","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1109\/TFUZZ.2008.924209","volume":"17","author":"R Jensen","year":"2009","unstructured":"Jensen R, Shen Q (2009) New approaches to fuzzy-rough feature selection. IEEE Trans Fuzzy Syst 17(4):824\u2013838","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"3760_CR44","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1109\/TFUZZ.2018.2862870","volume":"27","author":"AH Tan","year":"2019","unstructured":"Tan AH, Wu WZ, Qian YH, Liang JY, Chen JK, Li JJ (2019) Intuitionistic fuzzy rough set-based granular structures and attribute subset selection. IEEE Trans Fuzzy Syst 27(3):527\u2013539","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"3760_CR45","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.jbi.2017.02.007","volume":"67","author":"YM Chen","year":"2017","unstructured":"Chen YM, Zhang ZJ, Zheng JZ, Ma Y, Xue Y (2017) Gene selection for tumor classification using neighborhood rough sets and entropy measures. J Biomed Inform 67:59\u201368","journal-title":"J Biomed Inform"},{"issue":"6","key":"3760_CR46","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1016\/j.camwa.2008.10.027","volume":"57","author":"FF Xu","year":"2009","unstructured":"Xu FF, Miao DQ, Wei L (2009) Fuzzy-rough attribute reduction via mutual information with an application to cancer classification. Comput Math Appl 57(6):1010\u20131017","journal-title":"Comput Math Appl"},{"key":"3760_CR47","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.knosys.2018.03.015","volume":"151","author":"XD Fan","year":"2018","unstructured":"Fan XD, Zhao WD, Wang CZ, Huang Y (2018) Attribute reduction based on max-decision neighborhood rough set model. Knowl Based Syst 151:16\u201323","journal-title":"Knowl Based Syst"},{"issue":"2","key":"3760_CR48","first-page":"280","volume":"14","author":"W Zhang","year":"2018","unstructured":"Zhang W, Chen JJ (2018) Relief feature selection and parameter optimization for support vector machine based on mixed kernel function. Int J Perform Eng 14(2):280\u2013289","journal-title":"Int J Perform Eng"},{"key":"3760_CR49","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.neucom.2016.07.080","volume":"256","author":"HJ Lu","year":"2017","unstructured":"Lu HJ, Chen JY, Yan K, Jin Q, Xue Y, Gao ZG (2017) A hybrid feature selection algorithm for gene expression data classification. Neurocomputing 256:56\u201362","journal-title":"Neurocomputing"},{"issue":"6","key":"3760_CR50","doi-asserted-by":"publisher","first-page":"2028","DOI":"10.1109\/TCBB.2017.2761871","volume":"15","author":"JT Li","year":"2018","unstructured":"Li JT, Dong WP, Meng DY (2018) Grouped gene selection of cancer via adaptive sparse group lasso based on conditional mutual information. IEEE\/ACM Trans Comput Biol Bioinform 15(6):2028\u20132038","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"3760_CR51","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1016\/j.asoc.2015.10.037","volume":"38","author":"J Apolloni","year":"2016","unstructured":"Apolloni J, Leguizamon G, Alba E (2016) Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Appl Soft Computing 38:922\u2013932","journal-title":"Appl Soft Computing"},{"key":"3760_CR52","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of mrankings. Ann Math Stat 11:86\u201392","journal-title":"Ann Math Stat"},{"key":"3760_CR53","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1080\/01621459.1961.10482090","volume":"56","author":"OJ Dunn","year":"1961","unstructured":"Dunn OJ (1961) Multiple comparisons among means. Publications of the American Statistical Association 56:52\u201364","journal-title":"Publications of the American Statistical Association"},{"key":"3760_CR54","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1109\/TFUZZ.2019.2955047","volume":"28","author":"H Fujita","year":"2020","unstructured":"Fujita H, Gaeta A, Loia V, Orciuoli F (2020) Hypotheses Analysis and Assessment in Counterterrorism Activities: A Method Based on OWA and Fuzzy Probabilistic Rough Sets. IEEE Trans Fuzzy Syst 28:831\u2013845","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"3760_CR55","doi-asserted-by":"publisher","unstructured":"Xu JC, Qu KL, Meng XR, Sun YH, Hou QC (2022) Feature selection based on multiview entropy measures in multiperspective rough set. Int J Intell Syst. https:\/\/doi.org\/10.1002\/int.22878","DOI":"10.1002\/int.22878"},{"key":"3760_CR56","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1109\/TCYB.2018.2815178","volume":"49","author":"H Fujita","year":"2019","unstructured":"Fujita H, Gaeta A, Loia V, Orciuoli F (2019) Resilience Analysis of Critical Infrastructures: A Cognitive Approach Based on Granular Computing. IEEE Trans Cybern 49:1835\u20131848","journal-title":"IEEE Trans Cybern"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03760-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03760-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03760-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T15:41:18Z","timestamp":1675438878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03760-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,11]]},"references-count":56,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3760"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03760-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,11]]},"assertion":[{"value":"10 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}