{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:03:13Z","timestamp":1777705393094,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,30]]},"abstract":"<jats:p>Outlier detection is a process to find out the objects that have the abnormal behavior. It can be applied in many aspects, such as public security, finance and medical care. An information system (IS) as a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. A RVIS with missing values is an incomplete real-valued information system (IRVIS). The notion of inner boundary comes from the boundary region in rough set theory (RST). This paper conducts experiments directly in an IRVIS and investigates outlier detection in an IRVIS based on inner boundary. 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 relations on the object set are defined according to the distance, by the way, the tolerance classes, the \u03bb-lower and \u03bb-upper approximations in an IRVIS are put forward. Next, the inner boundary under each conditional attribute in an IRVIS is presented. The more inner boundaries an object belongs to, the more likely it is to be an outlier. Finally, an outlier detection method in an IRVIS based on inner boundary is proposed, and the corresponding algorithm (DE) is designed, where DE means degree of exceptionality. Through the experiments base on UCI Machine Learning Repository data sets, the DE algorithm is compared with other five algorithms. Experimental results show that DE algorithm has the better outlier detection effect in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the DE algorithm.<\/jats:p>","DOI":"10.3233\/jifs-222777","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T11:01:46Z","timestamp":1669114906000},"page":"3023-3041","source":"Crossref","is-referenced-by-count":1,"title":["Outlier detection for incomplete real-valued data based on inner boundary"],"prefix":"10.1177","volume":"44","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 Electronic Information, Guangxi Minzu University, Nanning, Guangxi, P.R. China"}]},{"given":"Zhaowen","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, Guangxi, P.R. China"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-222777_ref2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1214\/ss\/1042727940","article-title":"Statistical Fraud Detection: A Review Comment Comment Rejoinder","volume":"17","author":"Bolton","year":"2002","journal-title":"Statistical Science"},{"issue":"2","key":"10.3233\/JIFS-222777_ref4","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TKDE.2011.220","article-title":"A rough-set-basedincremental approach for updating approximations under dynamicmaintenance environments","volume":"25","author":"Chen","year":"2013","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"12","key":"10.3233\/JIFS-222777_ref5","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"},{"issue":"9","key":"10.3233\/JIFS-222777_ref6","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1109\/TCYB.2016.2636339","article-title":"Attributeselection for partially labeled categorical data by rough setapproach","volume":"47","author":"Dai","year":"2017","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"10.3233\/JIFS-222777_ref7","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/TSMCB.2012.2228480","article-title":"An uncertainty measure for incomplete decision tables and its applications","volume":"43","author":"Dai","year":"2013","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"C","key":"10.3233\/JIFS-222777_ref8","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"},{"issue":"9","key":"10.3233\/JIFS-222777_ref9","doi-asserted-by":"crossref","first-page":"6338","DOI":"10.1016\/j.eswa.2010.02.087","article-title":"An information entropy-based approach tooutlier detection in rough sets","volume":"37","author":"Feng","year":"2010","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-222777_ref11","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-222777_ref12","doi-asserted-by":"crossref","unstructured":"Hawkins S. , He H. , Williams G.J. and Baxter R.A. , Outlier detection using replicator neural networks. CiteSeer, International Conference on Data Ware housing and Knowledge Discovery, Springer, Berlin, Heidelberg (2002), 170\u2013180.","DOI":"10.1007\/3-540-46145-0_17"},{"issue":"9-10","key":"10.3233\/JIFS-222777_ref13","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"},{"key":"10.3233\/JIFS-222777_ref14","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TFUZZ.2005.864086","article-title":"Fuzzy probabilistic approximation spaces and their information measures","volume":"14","author":"Hu","year":"2006","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"2","key":"10.3233\/JIFS-222777_ref15","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10489-014-0591-4","article-title":"Outlier detection based on granular computing and rough set theory","volume":"42","author":"Jiang","year":"2015","journal-title":"Applied Intelligence"},{"key":"10.3233\/JIFS-222777_ref18","doi-asserted-by":"crossref","first-page":"4680","DOI":"10.1016\/j.eswa.2008.06.019","article-title":"Some issues about outlier detectionin rough set theory","volume":"36","author":"Jiang","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.3233\/JIFS-222777_ref19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.6339\/JDS.2013.11(1).1091","article-title":"A new procedure of clustering based onmultivariate outlier detection","volume":"11","author":"Jayakumar","year":"2013","journal-title":"Journal of Data Science"},{"issue":"9","key":"10.3233\/JIFS-222777_ref20","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-222777_ref21","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"},{"key":"10.3233\/JIFS-222777_ref22","first-page":"1517","article-title":"Generative adversarial active learning for unsupervised outlierdetection","volume":"32","author":"Liu","year":"2020","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-222777_ref23","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 roughsets","volume":"75","author":"Macia-Perez","year":"2015","journal-title":"Decision Support System"},{"issue":"12","key":"10.3233\/JIFS-222777_ref24","first-page":"248","article-title":"Novelty detection: a reviewpart 1, statistical approaches","volume":"83","author":"Markou","year":"2003","journal-title":"Signal Processing"},{"issue":"7","key":"10.3233\/JIFS-222777_ref26","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-222777_ref27","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"},{"key":"10.3233\/JIFS-222777_ref28","doi-asserted-by":"crossref","unstructured":"Pawlak Z. , Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht (1991).","DOI":"10.1007\/978-94-011-3534-4_7"},{"issue":"2","key":"10.3233\/JIFS-222777_ref30","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":"2","key":"10.3233\/JIFS-222777_ref31","first-page":"191","article-title":"Outlier detection based on rough sets theory","volume":"13","author":"Ramaswamy","year":"2000","journal-title":"Intelligent Data Analysis"},{"issue":"2","key":"10.3233\/JIFS-222777_ref32","doi-asserted-by":"crossref","first-page":"191","DOI":"10.3233\/IDA-2009-0363","article-title":"Outlier detection based onrough sets theory","volume":"13","author":"Shaari","year":"2009","journal-title":"Intelligent Data Analysis"},{"key":"10.3233\/JIFS-222777_ref33","doi-asserted-by":"crossref","first-page":"100027","DOI":"10.1016\/j.socl.2021.100027","article-title":"A fuzzy proximity relation approachfor outlier detection in the mixed dataset by using roughentropy-based weighted density method","volume":"3","author":"Sangeetha","year":"2021","journal-title":"Soft Computing Letters"},{"key":"10.3233\/JIFS-222777_ref34","doi-asserted-by":"crossref","unstructured":"Sabokrou M. , Khalooei M. , Fathy M. and Adeli E. , Adversarially learned one-class classifier for novelty detection, in: IEEE Conference on Computer Vision and Pattern Recognition (2018), 3379\u20133388.","DOI":"10.1109\/CVPR.2018.00356"},{"issue":"9","key":"10.3233\/JIFS-222777_ref35","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":"IEEETransactions on Cybernetics"},{"key":"10.3233\/JIFS-222777_ref36","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.inffus.2019.02.006","article-title":"Outlier detection based on a dynamic ensemblemodel: Applied to process monitoring","volume":"51","author":"Wang","year":"2019","journal-title":"Information Fusion"},{"key":"10.3233\/JIFS-222777_ref37","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1109\/TFUZZ.2019.2953024","article-title":"Fusingfuzzy monotonic decision trees","volume":"28","author":"Wang","year":"2020","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"10.3233\/JIFS-222777_ref38","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TFUZZ.2019.2949765","article-title":"Fuzzy roughattribute reduction for categorical data","volume":"28","author":"Wang","year":"2020","journal-title":"IEEE Transactions onFuzzy Systems"},{"key":"10.3233\/JIFS-222777_ref41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fss.2020.10.017","article-title":"Fuzzy informationentropy-based adaptive approach for hybrid feature outlierdetection","volume":"421","author":"Yuan","year":"2021","journal-title":"Fuzzy Sets and Systems"},{"key":"10.3233\/JIFS-222777_ref43","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.fss.2016.08.001","article-title":"Fuzzyrough set based incremental attribute reduction from dynamic datawith sample arriving","volume":"312","author":"Yang","year":"2017","journal-title":"Fuzzy Sets and Systems"},{"key":"10.3233\/JIFS-222777_ref44","first-page":"81","article-title":"Outlier detection algorithm based onneighborhood value difference metric","volume":"38","author":"Yuan","year":"2018","journal-title":"Journal of ComputationalAnd Applied Mathematics"},{"issue":"6","key":"10.3233\/JIFS-222777_ref45","first-page":"1317","article-title":"Sequence-based mixed attributeoutlier detection in neighborhood rough sets","volume":"39","author":"Yuan","year":"2018","journal-title":"J. Chin. Comput.Syst"},{"key":"10.3233\/JIFS-222777_ref46","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.eswa.2018.06.013","article-title":"Hybrid data-driven outlierdetection based on neighborhood information entropy and itsdevelopmental measures","volume":"112","author":"Yuan","year":"2018","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"10.3233\/JIFS-222777_ref47","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"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-222777","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:43Z","timestamp":1777455823000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-222777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":37,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-222777","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,30]]}}}