{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:29:32Z","timestamp":1777696172353,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2022,4,18]]},"abstract":"<jats:p>Classification of imbalanced data is an important challenge in current research. Sampling is an important way to solve the problem of imbalanced data classification, but some traditional sampling algorithms are susceptible to outliers. Therefore, an iF-ADASYN sampling algorithm is proposed in this paper. First, based on the ADASYN algorithm, we introduce the isolation Forest algorithm to overcome its vulnerability to outliers. Then, a calculation method of anomaly index which can delete outliers accurately of minority data is presented. The experimental results of four UCI public imbalanced datasets show that the algorithm can effectively improve the accuracy of the minority class, and increase the stability. In the real thrombus dataset, the AUC value of the iF-ADASYN algorithm is more significant than that of SMOTE and ADASYN algorithms, and the recognition rate of patients with thrombosis increased by 20%. The iF-ADASYN algorithm obtains better resistance to outliers than the original ADASYN algorithm. Meanwhile, it improves the accuracy of minority class decision boundary region division.<\/jats:p>","DOI":"10.3233\/ida-211519","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T13:25:35Z","timestamp":1650979535000},"page":"583-598","source":"Crossref","is-referenced-by-count":3,"title":["A resistance outlier sampling algorithm for imbalanced data prediction"],"prefix":"10.1177","volume":"26","author":[{"given":"Xiaoying","family":"Pan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"},{"name":"The Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Northwestern Polytechnical University, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/IDA-211519_ref2","first-page":"148","article-title":"Review of the research on imbalanced classification","volume":"4","author":"Ye","year":"2009","journal-title":"Journal of Intelligent Systems"},{"issue":"24","key":"10.3233\/IDA-211519_ref3","first-page":"2238","article-title":"Application review and obstacle analysis of evidence for prevention and management of deep venous thrombosis in patients after spinal orthopedic surgery","volume":"34","author":"Chen","year":"2019","journal-title":"Journal of Nurses Further Studies"},{"issue":"020","key":"10.3233\/IDA-211519_ref4","first-page":"57","article-title":"Prevention and treatment of deep venous thrombosis of lower limbs in patients after orthopedic trauma surgery","volume":"002","author":"Wu","year":"2017","journal-title":"Clinical Research and Practice"},{"key":"10.3233\/IDA-211519_ref5","unstructured":"H. He, B. Yang, E.A. Garcia et al., ADASYN: Adaptive synthetic sampling approach for imbalanced learning, 2008 IEEE International Joint Conference on Neural Networks, 2008."},{"issue":"3","key":"10.3233\/IDA-211519_ref6","doi-asserted-by":"crossref","first-page":"83","DOI":"10.23919\/JCC.2019.10.006","article-title":"An efficient outlier detection approach on weighted data stream based on minimal rare pattern mining","volume":"16","author":"Cai","year":"2019","journal-title":"China Communications"},{"key":"10.3233\/IDA-211519_ref8","doi-asserted-by":"crossref","unstructured":"M. Ektefa, S. Memar, F. Sidi et al., Intrusion detection using data mining techniques, International Conference on Information Retrieval & Knowledge Management. EEE, 2010.","DOI":"10.1109\/INFRKM.2010.5466919"},{"key":"10.3233\/IDA-211519_ref9","doi-asserted-by":"crossref","unstructured":"H.M. Nguyen, E.W. Cooper and K. Kamei, A comparative study on sampling techniques for handling class imbalance in streaming data, Joint International Conference on Soft Computing & Intelligent Systems, IEEE, 2012.","DOI":"10.1109\/SCIS-ISIS.2012.6505291"},{"issue":"5","key":"10.3233\/IDA-211519_ref10","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1109\/TNN.2009.2018547","article-title":"A novel template reduction approach for the -nearest neighbor method","volume":"20","author":"Fayed","year":"2009","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"1","key":"10.3233\/IDA-211519_ref11","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explorations Newsletter"},{"issue":"4","key":"10.3233\/IDA-211519_ref12","first-page":"536","article-title":"Unbalanced fuzzy multi-class support vector machine based on class overlap undersampling","volume":"35","author":"Wu","year":"2018","journal-title":"Journal of University of Chinese Academy of Sciences"},{"issue":"5","key":"10.3233\/IDA-211519_ref13","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1016\/j.patcog.2013.05.006","article-title":"EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling","volume":"46","author":"Galar","year":"2013","journal-title":"Pattern Recognition"},{"issue":"1","key":"10.3233\/IDA-211519_ref14","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"3","key":"10.3233\/IDA-211519_ref15","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s10994-017-5670-4","article-title":"Manifold-based synthetic oversampling with manifold conformance estimation","volume":"107","author":"Bellinger","year":"2018","journal-title":"Machine Learning"},{"issue":"5","key":"10.3233\/IDA-211519_ref16","first-page":"1017","article-title":"Oversampling method for imbalanced classification","volume":"34","author":"Zheng","year":"2016","journal-title":"Comput Info"},{"issue":"5","key":"10.3233\/IDA-211519_ref17","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.eswa.2015.10.031","article-title":"Adaptive semi-unsupervised weighted oversampling (A-SUWO) for Imbalanced Datasets","volume":"46","author":"Nekooeimehr","year":"2016","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-211519_ref18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","article-title":"Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE","volume":"465","author":"Georgios","year":"2018","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-211519_ref20","first-page":"57","article-title":"A resampling ensemble algorithm for classification of imbalance problems","volume":"143","author":"Qian","year":"2014","journal-title":"Neuro Computing"},{"key":"10.3233\/IDA-211519_ref21","first-page":"3","article-title":"Addressing imbalance in multilabel classification: Measures and random resampling algorithms","volume":"163","author":"Charte","year":"2015","journal-title":"Neuro Computing"},{"key":"10.3233\/IDA-211519_ref22","doi-asserted-by":"crossref","unstructured":"M. Wang, X. Yao and Y. Chen, An Imbalanced-Data Processing Algorithm for the Prediction of Heart Attack in Stroke Patients, IEEE Access, 2021, pp.\u00a01.","DOI":"10.1109\/ACCESS.2021.3057693"},{"issue":"1","key":"10.3233\/IDA-211519_ref24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2133360.2133363","article-title":"Isolation-based anomaly detection","volume":"6","author":"Liu","year":"2012","journal-title":"Acm Transactions on Knowledge Discovery from Data"},{"issue":"4","key":"10.3233\/IDA-211519_ref25","first-page":"10","article-title":"Research and advancement of classification method of imbalanced data sets","volume":"35","author":"Wei","year":"2008","journal-title":"Computer Science"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-211519","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:19:27Z","timestamp":1777454367000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-211519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,18]]},"references-count":21,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/ida-211519","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,18]]}}}