{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T14:03:31Z","timestamp":1784988211154,"version":"3.55.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032302274","type":"print"},{"value":"9783032302281","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T00:00:00Z","timestamp":1785024000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T00:00:00Z","timestamp":1785024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-30228-1_6","type":"book-chapter","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T13:30:31Z","timestamp":1784986231000},"page":"81-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of a Supervised Fuzzy Object Selection Algorithm for Outlier Identification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6446-036X","authenticated-orcid":false,"given":"Wies\u0142aw","family":"Paja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2133-4187","authenticated-orcid":false,"given":"Jaromir","family":"Sarzy\u0144ski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1225-9143","authenticated-orcid":false,"given":"Micha\u0142","family":"K\u0119pski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,26]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/3-540-45681-3_2","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"F Angiulli","year":"2002","unstructured":"Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) Principles of Data Mining and Knowledge Discovery, pp. 15\u201327. Springer, Berlin, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45681-3_2"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wr\u00f3blewski, J.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, pp. 49\u201388. Physica-Verlag, Heidelberg (2000). https:\/\/doi.org\/10.1007\/978-3-7908-1840-6_3","DOI":"10.1007\/978-3-7908-1840-6_3"},{"key":"6_CR3","doi-asserted-by":"publisher","unstructured":"Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. 53(3) (2020). https:\/\/doi.org\/10.1145\/3381028","DOI":"10.1145\/3381028"},{"key":"6_CR4","doi-asserted-by":"publisher","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD \u201900, pp. 93\u2013104. Association for Computing Machinery, New York, NY, USA (2000). https:\/\/doi.org\/10.1145\/342009.335388","DOI":"10.1145\/342009.335388"},{"key":"6_CR5","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226\u2013231 (1996)"},{"key":"6_CR6","unstructured":"Goldstein, M., Dengel, A.: Histogram-based outlier score (HBOS): a fast unsupervised anomaly detection algorithm. KI-2012: Poster and Demo Track 1, 59\u201363 (2012)"},{"key":"6_CR7","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/978-3-030-43887-6_14","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"G Kaiafas","year":"2020","unstructured":"Kaiafas, G., Hammerschmidt, C., Lagraa, S., State, R.: Auto semi-supervised outlier detection for malicious authentication events. In: Cellier, P., Driessens, K. (eds.) ECML PKDD 2019. CCIS, vol. 1168, pp. 176\u2013190. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43887-6_14"},{"key":"6_CR8","doi-asserted-by":"publisher","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422 (2008). https:\/\/doi.org\/10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"issue":"2","key":"6_CR9","doi-asserted-by":"publisher","first-page":"2774","DOI":"10.3390\/s150202774","volume":"15","author":"L Mart\u00ed","year":"2015","unstructured":"Mart\u00ed, L., Sanchez-Pi, N., Molina, J.M., Garcia, A.C.B.: Anomaly detection based on sensor data in petroleum industry applications. Sensors 15(2), 2774\u20132797 (2015). https:\/\/doi.org\/10.3390\/s150202774","journal-title":"Sensors"},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Paja, W.: Identification of relevant medical parameter values in information systems using fuzzy approach. Procedia Comput. Sci. 192, 3915\u20133921 (2021). https:\/\/doi.org\/10.1016\/j.procs.2021.09.166, knowledge-Based and Intelligent Information Engineering Systems: Proceedings of the 25th International Conference KES2021","DOI":"10.1016\/j.procs.2021.09.166"},{"key":"6_CR11","doi-asserted-by":"publisher","unstructured":"Paja, W.: Application of the fuzzy approach for evaluating and selecting relevant objects, features, and their ranges. Entropy 25(8) (2023). https:\/\/doi.org\/10.3390\/e25081223","DOI":"10.3390\/e25081223"},{"key":"6_CR12","doi-asserted-by":"publisher","unstructured":"Paja, W., Pancerz, K., P\u0119kala, B., Sarzy\u0144ski, J.: Application of the fuzzy logic to evaluation and selection of attribute ranges in machine learning. In: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp.\u00a01\u20136 (2021). https:\/\/doi.org\/10.1109\/FUZZ45933.2021.9494515","DOI":"10.1109\/FUZZ45933.2021.9494515"},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1016\/j.procs.2018.07.245","volume":"126","author":"K Pancerz","year":"2018","unstructured":"Pancerz, K., Paja, W., Sarzy\u0144ski, J., Gomu\u0142a, J.: Determining importance of ranges of MMPI scales using fuzzification and relevant attribute selection. Procedia Comput. Sci. 126, 2065\u20132074 (2018). https:\/\/doi.org\/10.1016\/j.procs.2018.07.245","journal-title":"Procedia Comput. Sci."},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2) (2021). https:\/\/doi.org\/10.1145\/3439950","DOI":"10.1145\/3439950"},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"100306","DOI":"10.1016\/j.cosrev.2020.100306","volume":"38","author":"A Smiti","year":"2020","unstructured":"Smiti, A.: A critical overview of outlier detection methods. Comput. Sci. Rev. 38, 100306 (2020). https:\/\/doi.org\/10.1016\/j.cosrev.2020.100306","journal-title":"Comput. Sci. Rev."},{"key":"6_CR16","doi-asserted-by":"publisher","unstructured":"Stojanovi\u0107, B., et al.: Follow the trail: Machine learning for fraud detection in fintech applications. Sensors 21(5) (2021). https:\/\/doi.org\/10.3390\/s21051594","DOI":"10.3390\/s21051594"},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"107964","DOI":"10.1109\/ACCESS.2019.2932769","volume":"7","author":"H Wang","year":"2019","unstructured":"Wang, H., Bah, M.J., Hammad, M.: Progress in outlier detection techniques: a survey. IEEE Access 7, 107964\u2013108000 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2932769","journal-title":"IEEE Access"},{"key":"6_CR18","doi-asserted-by":"publisher","unstructured":"Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. SMC-2(3), 408\u2013421 (1972). https:\/\/doi.org\/10.1109\/TSMC.1972.4309137","DOI":"10.1109\/TSMC.1972.4309137"},{"key":"6_CR19","unstructured":"Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)"}],"container-title":["Lecture Notes in Computer Science","Rough Sets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-30228-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T13:30:32Z","timestamp":1784986232000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-30228-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,26]]},"ISBN":["9783032302274","9783032302281"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-30228-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,26]]},"assertion":[{"value":"26 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IJCRS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Rough Sets","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"C\u00e1diz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcrs2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ijcrs2026.uca.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}