{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:49:23Z","timestamp":1777704563993,"version":"3.51.4"},"reference-count":39,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>Outlier detection is an important topic in data mining. An information system (IS) is a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. People often encounter missing values during data processing. A RVIS with the miss values is an incomplete real-valued information system (IRVIS). Due to the presence of the missing values, the distance between two information values is difficult to determine, so the existing outlier detection rarely considered an IS with the miss values. This paper investigates outlier detection for an IRVIS via rough set theory and granular computing. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter \u03bb to control the distance is given. Then, the tolerance relation on the object set is defined according to the distance, and the tolerance class is obtained, which is regarded as an information granule. After then, \u03bb-lower and \u03bb-upper approximations in an IRVIS are put forward. Next, the outlier factor of every object in an IRVIS is presented. Finally, outlier detection method for IRVIS via rough set theory and granular computing is proposed, and the corresponding algorithms is designed. Through the experiments, the proposed method is compared with other methods. The experimental results show that the designed algorithm is more effective than some existing algorithms in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the proposed method.<\/jats:p>","DOI":"10.3233\/jifs-230737","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T11:00:19Z","timestamp":1690887619000},"page":"6247-6271","source":"Crossref","is-referenced-by-count":1,"title":["Outlier detection for incomplete real-valued data via rough set theory and granular computing"],"prefix":"10.1177","volume":"45","author":[{"given":"Zhengwei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics, Guangxi Minzu University, Nanning, Guangxi, P.R. China"}]},{"given":"Genteng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Physics, Guangxi Minzu University, Nanning, Guangxi, P.R. China"}]},{"given":"Zhaowen","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Applied Mathematics of Guangxi, Yulin Normal University, Yulin, Guangxi, P.R. China"}]},{"given":"Guangji","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Big Data and Artificial Intelligence, Guangxi University of Finance and Economics, Nanning, Guangxi, P.R. China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-230737_ref2","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.ins.2021.04.016","article-title":"An improvement of rough setsa\u0155 accuracy measure using containment neighborhoods with a medical application","volume":"569","author":"Al-Shami","year":"2021","journal-title":"Information Sciences"},{"key":"10.3233\/JIFS-230737_ref3","doi-asserted-by":"crossref","first-page":"4101","DOI":"10.1007\/s40747-022-00704-x","article-title":"Topological approach to generate new rough set models","volume":"8","author":"Al-Shami","year":"2022","journal-title":"Complex & Intelligent Systems"},{"key":"10.3233\/JIFS-230737_ref4","doi-asserted-by":"crossref","first-page":"14449","DOI":"10.1007\/s00500-021-06358-0","article-title":"Improvement of the approximations and accuracy measure of a rough set using somewhere dense sets","volume":"25","author":"Al-Shami","year":"2021","journal-title":"Soft Computing"},{"key":"10.3233\/JIFS-230737_ref6","doi-asserted-by":"crossref","first-page":"107868","DOI":"10.1016\/j.knosys.2021.107868","article-title":"Subset neighborhood rough sets","volume":"237","author":"Al-Shami","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/JIFS-230737_ref7","doi-asserted-by":"crossref","first-page":"79379","DOI":"10.1109\/ACCESS.2022.3194562","article-title":"Improvement of approximation spaces using maximal left neighborhoods and ideals","volume":"10","author":"Al-Shami","year":"2022","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-230737_ref8","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1007\/s00500-022-07627-2","article-title":"Approximation operators and accuracy measures of rough sets from an infra-topology view","volume":"27","author":"Al-Shami","year":"2023","journal-title":"Soft Computing"},{"issue":"2","key":"10.3233\/JIFS-230737_ref9","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00778-004-0125-5","article-title":"An effective and efficient algorithm for high-dimensional outlier detection","volume":"14","author":"Aggarwal","year":"2005","journal-title":"VLDB J"},{"key":"10.3233\/JIFS-230737_ref12","unstructured":"Barnett V. and Lewis T. , Outliers in statistical data, John Wiley and Sons, New York, 1994."},{"key":"10.3233\/JIFS-230737_ref13","doi-asserted-by":"crossref","unstructured":"Chandola V. , Banerjee A. and Kumar V. , Outlier detection: A survey, ACM Computing Surveys 41(3) (2009).","DOI":"10.1145\/1541880.1541882"},{"issue":"2","key":"10.3233\/JIFS-230737_ref14","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TKDE.2011.220","article-title":"A rough-set-based incremental approach for updating approximations under dynamic maintenance environments","volume":"25","author":"Chen","year":"2013","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"12","key":"10.3233\/JIFS-230737_ref15","doi-asserted-by":"crossref","first-page":"8745","DOI":"10.1016\/j.eswa.2010.06.040","article-title":"Neighborhood outlier detection","volume":"37","author":"Chen","year":"2010","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-230737_ref16","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.patcog.2017.09.037","article-title":"A comparative evaluation of outlier detection algorithms: Experiments and analyses","volume":"74","author":"Domingues","year":"2018","journal-title":"Pattern Recognition"},{"issue":"9","key":"10.3233\/JIFS-230737_ref17","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1109\/TCYB.2016.2636339","article-title":"Attribute selection for partially labeled categorical data by rough set approach","volume":"47","author":"Dai","year":"2017","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.3233\/JIFS-230737_ref18","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.ins.2023.03.120","article-title":"Outlier detection in social networks leveraging community structure","volume":"634","author":"Dey","year":"2023","journal-title":"Information Sciences"},{"issue":"C","key":"10.3233\/JIFS-230737_ref19","first-page":"63","article-title":"Algorithm for the detection of outliers based on the theory of rough sets","volume":"75","author":"Francisco","year":"2015","journal-title":"Decision Support Systems"},{"key":"10.3233\/JIFS-230737_ref20","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.ins.2022.11.154","article-title":"A relative granular ratio-based outlier detection method in heterogeneous data","volume":"622","author":"Gao","year":"2023","journal-title":"Information Sciences"},{"key":"10.3233\/JIFS-230737_ref21","doi-asserted-by":"crossref","unstructured":"Hawkins D.M. , Identification of outliers, Chapman and Hall, London, 1980.","DOI":"10.1007\/978-94-015-3994-4"},{"key":"10.3233\/JIFS-230737_ref22","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.aej.2023.02.008","article-title":"Novel approaches of generalized rough approximation spaces inspired by maximal neighbourhoods and ideals","volume":"69","author":"Hosny","year":"2023","journal-title":"Alexandria Engineering Journal"},{"issue":"9-10","key":"10.3233\/JIFS-230737_ref24","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1016\/S0167-8655(03)00003-5","article-title":"Discovering cluster-based local outliers","volume":"24","author":"He","year":"2003","journal-title":"Pattern Recognition Letters"},{"issue":"3","key":"10.3233\/JIFS-230737_ref25","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: a review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Computing Surveys"},{"issue":"9","key":"10.3233\/JIFS-230737_ref27","doi-asserted-by":"crossref","first-page":"6338","DOI":"10.1016\/j.eswa.2010.02.087","article-title":"An information entropy-based approach to outlier detection in rough sets","volume":"37","author":"Jiang","year":"2010","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-230737_ref28","doi-asserted-by":"crossref","first-page":"4680","DOI":"10.1016\/j.eswa.2008.06.019","article-title":"Some issues about outlier detection in rough set theory","volume":"36","author":"Jiang","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"9","key":"10.3233\/JIFS-230737_ref29","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1007\/s13042-018-0884-8","article-title":"Outlier detection based on approximation accuracy entropy","volume":"10","author":"Jiang","year":"2018","journal-title":"International Journal of Machine Learning and Cybernetics"},{"issue":"3","key":"10.3233\/JIFS-230737_ref30","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s007780050006","article-title":"Distance-based outliers: algorithms and applications","volume":"8","author":"Knorr","year":"2000","journal-title":"The VLDB Journal"},{"issue":"8","key":"10.3233\/JIFS-230737_ref31","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10916-018-1003-9","article-title":"A survey of data mining and deep learning in bioinformatics","volume":"42","author":"Lan","year":"2018","journal-title":"Journal of Medical Systems"},{"issue":"2","key":"10.3233\/JIFS-230737_ref32","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1023\/A:1008384328214","article-title":"Data Mining and Machine Oriented Modeling: A Granular Computing Approach","volume":"13","author":"Lin","year":"2000","journal-title":"Applied Intelligence"},{"issue":"9","key":"10.3233\/JIFS-230737_ref33","doi-asserted-by":"crossref","first-page":"106186","DOI":"10.1016\/j.knosys.2020.106186","article-title":"Scalable KDE-based top-n local outlier detection over large-scale data streams","volume":"204","author":"Liu","year":"2020","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/JIFS-230737_ref35","doi-asserted-by":"crossref","first-page":"4683","DOI":"10.2298\/FIL2314683M","article-title":"Rough set paradigms via containment neighborhoods and ideals","volume":"37","author":"Mustafaa","year":"2023","journal-title":"Filomat"},{"key":"10.3233\/JIFS-230737_ref36","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.dss.2015.05.002","article-title":"Algorithm for the detection of outliers based on the theory of rough sets","volume":"75","author":"Macia-Perez","year":"2015","journal-title":"Decision Support System"},{"issue":"7","key":"10.3233\/JIFS-230737_ref37","doi-asserted-by":"crossref","first-page":"2690","DOI":"10.1016\/j.patcog.2011.12.027","article-title":"Class-dependent rough-fuzzy granular space, dispersion index and classification","volume":"45","author":"Pal","year":"2012","journal-title":"Pattern Recognition"},{"key":"10.3233\/JIFS-230737_ref39","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/BF01001956","article-title":"Rough sets","volume":"11","author":"Pawlak","year":"1982","journal-title":"International Journal of Computer and Information Science"},{"issue":"2","key":"10.3233\/JIFS-230737_ref41","first-page":"260","article-title":"Robust regression and outlier detection","volume":"31","author":"Rousseeuw","year":"1987","journal-title":"Journal of the American Statistical Association"},{"issue":"9","key":"10.3233\/JIFS-230737_ref43","doi-asserted-by":"crossref","first-page":"4031","DOI":"10.1109\/TCYB.2019.2923430","article-title":"Feature selection based on neighborhood self-information","volume":"50","author":"Wang","year":"2020","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.3233\/JIFS-230737_ref46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fss.2020.10.017","article-title":"Fuzzy information entropy-based adaptive approach for hybrid feature outlier detection","volume":"421","author":"Yuan","year":"2021","journal-title":"Fuzzy Sets and Systems"},{"issue":"8","key":"10.3233\/JIFS-230737_ref47","doi-asserted-by":"crossref","first-page":"8399","DOI":"10.1109\/TCYB.2021.3058780","article-title":"Outlier detection based on fuzzy rough granules in mixed attribute data","volume":"52","author":"Yuan","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.3233\/JIFS-230737_ref48","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1109\/TSMCC.2012.2236648","article-title":"Granular computing: Perspectives and challenges","volume":"43","author":"Yao","year":"2013","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"2","key":"10.3233\/JIFS-230737_ref49","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0165-0114(97)00077-8","article-title":"Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic","volume":"90","author":"Zadeh","year":"1997","journal-title":"Fuzzy Sets and Systems"},{"key":"10.3233\/JIFS-230737_ref50","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/TFUZZ.2014.2327993","article-title":"A novel approach to building a robust fuzzy rough classifier","volume":"23","author":"Zhao","year":"2015","journal-title":"IEEE Transactions on Fuzzy System"},{"key":"10.3233\/JIFS-230737_ref51","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.1109\/TSMC.2016.2574538","article-title":"Measuring uncertainty of probabilistic rough set model from its three regions","volume":"47","author":"Zhang","year":"2017","journal-title":"IEEE Transactions on Systems, Man and Cybernetics (Part A)"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-230737","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:29Z","timestamp":1777455689000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-230737"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,4]]},"references-count":39,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/jifs-230737","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,4]]}}}