{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:25:23Z","timestamp":1778347523810,"version":"3.51.4"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031101601","type":"print"},{"value":"9783031101618","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-10161-8_14","type":"book-chapter","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T20:02:54Z","timestamp":1658174574000},"page":"250-268","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Tsetlin Machine Framework for\u00a0Universal Outlier and\u00a0Novelty Detection"],"prefix":"10.1007","author":[{"given":"Bimal","family":"Bhattarai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ole-Christoffer","family":"Granmo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"key":"14_CR1","unstructured":"Abeyrathna, K.D., et al.: Massively parallel and asynchronous Tsetlin Machine architecture supporting almost constant-time scaling. In: The Thirty-Eighth International Conference on Machine Learning (ICML 2021) (2021)"},{"issue":"2164","key":"14_CR2","doi-asserted-by":"publisher","first-page":"20190165","DOI":"10.1098\/rsta.2019.0165","volume":"378","author":"KD Abeyrathna","year":"2019","unstructured":"Abeyrathna, K.D., Granmo, O.C., Zhang, X., Jiao, L., Goodwin, M.: The regression tsetlin machine: a novel approach to interpretable nonlinear regression. Phil. Trans. R. Soc. A 378(2164), 20190165 (2019)","journal-title":"Phil. Trans. R. Soc. A"},{"key":"14_CR3","doi-asserted-by":"publisher","unstructured":"Achtert, E., Kriegel, H.-P., Zimek, A.: ELKI: a software system for evaluation of subspace clustering algorithms. In: Lud\u00e4scher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 580\u2013585. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-69497-7_41","DOI":"10.1007\/978-3-540-69497-7_41"},{"key":"14_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-47578-3_1","volume-title":"Outlier Analysis","author":"CC Aggarwal","year":"2017","unstructured":"Aggarwal, C.C.: An introduction to outlier analysis. In: Outlier Analysis, pp. 1\u201334. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-47578-3_1"},{"key":"14_CR5","doi-asserted-by":"publisher","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Banerjee, P., Yawalkar, P., Ranu, S.: Mantra: a scalable approach to mining temporally anomalous sub-trajectories. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1415\u20131424 (2016)","DOI":"10.1145\/2939672.2939846"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 59\u201368 (2004)","DOI":"10.1145\/1014052.1014062"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.E.: Towards open set deep networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.173"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1563\u20131572 (2016)","DOI":"10.1109\/CVPR.2016.173"},{"key":"14_CR10","doi-asserted-by":"publisher","first-page":"115134","DOI":"10.1109\/ACCESS.2019.2935416","volume":"7","author":"GT Berge","year":"2019","unstructured":"Berge, G.T., Granmo, O.C., Tveit, T.O., Goodwin, M., Jiao, L., Matheussen, B.V.: Using the tsetlin machine to learn human-interpretable rules for high-accuracy text categorization with medical applications. IEEE Access 7, 115134\u2013115146 (2019)","journal-title":"IEEE Access"},{"key":"14_CR11","unstructured":"Bhattarai, B., Granmo, O.C., Jiao, L.: Explainable tsetlin machine framework for fake news detection with credibility score assessment. arXiv preprint arXiv:2105.09114 (2021)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Bhattarai, B., Granmo, O.C., Jiao, L.: Measuring the novelty of natural language text using the conjunctive clauses of a tsetlin machine text classifier. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, pp. 410\u2013417 (2021)","DOI":"10.5220\/0010382204100417"},{"key":"14_CR13","doi-asserted-by":"publisher","unstructured":"Bhattarai, B., Granmo, O.C., Jiao, L.: Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines (2021). https:\/\/doi.org\/10.1007\/s10489-022-03281-1. arXiv preprint arXiv:2105.04708","DOI":"10.1007\/s10489-022-03281-1"},{"key":"14_CR14","doi-asserted-by":"crossref","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, pp. 93\u2013104 (2000)","DOI":"10.1145\/335191.335388"},{"issue":"4","key":"14_CR15","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s10618-015-0444-8","volume":"30","author":"GO Campos","year":"2016","unstructured":"Campos, G.O., et al.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Disc. 30(4), 891\u2013927 (2016). https:\/\/doi.org\/10.1007\/s10618-015-0444-8","journal-title":"Data Min. Knowl. Disc."},{"issue":"3","key":"14_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1\u201358 (2009)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"14_CR17","unstructured":"Cohen, G., Sax, H., Geissbuhler, A., et al.: Novelty detection using one-class parzen density estimator. An application to surveillance of nosocomial infections. In: Mie, pp. 21\u201326 (2008)"},{"key":"14_CR18","doi-asserted-by":"publisher","unstructured":"Craswell, N.: Precision at n. In: Encyclopedia of Database Systems (2009). https:\/\/doi.org\/10.1007\/978-0-387-39940-9_484","DOI":"10.1007\/978-0-387-39940-9_484"},{"issue":"1","key":"14_CR19","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10479-008-0371-9","volume":"168","author":"L Duan","year":"2009","unstructured":"Duan, L., Xu, L., Liu, Y., Lee, J.: Cluster-based outlier detection. Ann. Oper. Res. 168(1), 151\u2013168 (2009). https:\/\/doi.org\/10.1007\/s10479-008-0371-9","journal-title":"Ann. Oper. Res."},{"key":"14_CR20","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","volume":"58","author":"SM Erfani","year":"2016","unstructured":"Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recogn. 58, 121\u2013134 (2016)","journal-title":"Pattern Recogn."},{"key":"14_CR21","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: kdd, vol. 96, pp. 226\u2013231 (1996)"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Fei, G., Liu, B.: Social media text classification under negative covariate shift. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2347\u20132356 (2015)","DOI":"10.18653\/v1\/D15-1282"},{"key":"14_CR23","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.ins.2017.12.030","volume":"479","author":"U Fiore","year":"2019","unstructured":"Fiore, U., De Santis, A., Perla, F., Zanetti, P., Palmieri, F.: Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Inf. Sci. 479, 448\u2013455 (2019)","journal-title":"Inf. Sci."},{"key":"14_CR24","unstructured":"Goldstein, M., Dengel, A.: Histogram-based outlier score (hbos): a fast unsupervised anomaly detection algorithm. KI-2012: Poster and Demo Track, pp. 59\u201363 (2012)"},{"key":"14_CR25","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. Advances in neural information processing systems, 27 (2014)"},{"issue":"11","key":"14_CR26","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"14_CR27","unstructured":"Granmo, O.C.: The Tsetlin machine - a game theoretic bandit driven approach to optimal pattern recognition with propositional logic. ArXiv abs\/1804.01508 (2018)"},{"key":"14_CR28","unstructured":"Granmo, O.C., Glimsdal, S., Jiao, L., Goodwin, M., Omlin, C.W., Berge, G.T.: The convolutional tsetlin machine. arXiv preprint arXiv:1905.09688 (2019)"},{"issue":"1","key":"14_CR29","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29\u201336 (1982)","journal-title":"Radiology"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Hautamaki, V., Karkkainen, I., Franti, P.: Outlier detection using k-nearest neighbour graph. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 3, pp. 430\u2013433. IEEE (2004)","DOI":"10.1109\/ICPR.2004.1334558"},{"issue":"9\u201310","key":"14_CR31","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1016\/S0167-8655(03)00003-5","volume":"24","author":"Z He","year":"2003","unstructured":"He, Z., Xu, X., Deng, S.: Discovering cluster-based local outliers. Pattern Recogn. Lett. 24(9\u201310), 1641\u20131650 (2003)","journal-title":"Pattern Recogn. Lett."},{"key":"14_CR32","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/978-3-540-87479-9_51","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"K Hempstalk","year":"2008","unstructured":"Hempstalk, K., Frank, E., Witten, I.H.: One-class classification by combining density and class probability estimation. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5211, pp. 505\u2013519. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-87479-9_51"},{"key":"14_CR33","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)"},{"key":"14_CR34","doi-asserted-by":"crossref","unstructured":"Jiao, L., Zhang, X., Granmo, O.C., Abeyrathna, K.D.: On the convergence of tsetlin machines for the XOR operator. arXiv preprint arXiv:2101.02547 (2021)","DOI":"10.1109\/TPAMI.2022.3203150"},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Kowsari, K., Brown, D., Heidarysafa, M., Meimandi, K., Gerber, M., Barnes, L.: Hdltex: hierarchical deep learning for text classification. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 364\u2013371 (2017)","DOI":"10.1109\/ICMLA.2017.0-134"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Kriegel, H.P., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 444\u2013452 (2008)","DOI":"10.1145\/1401890.1401946"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhao, Y., Botta, N., Ionescu, C., Hu, X.: Copod: copula-based outlier detection. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 1118\u20131123. IEEE (2020)","DOI":"10.1109\/ICDM50108.2020.00135"},{"key":"14_CR38","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"},{"issue":"8","key":"14_CR39","first-page":"1517","volume":"32","author":"Y Liu","year":"2019","unstructured":"Liu, Y., et al.: Generative adversarial active learning for unsupervised outlier detection. IEEE Trans. Knowl. Data Eng. 32(8), 1517\u20131528 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"14_CR40","doi-asserted-by":"crossref","unstructured":"Mao, J., Wang, T., Jin, C., Zhou, A.: Feature grouping-based outlier detection upon streaming trajectories. IEEE Trans. Knowl. Data Eng. 29(12), 2696\u20132709 (2017)","DOI":"10.1109\/TKDE.2017.2744619"},{"issue":"5","key":"14_CR41","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1109\/TKDE.2014.2365790","volume":"27","author":"M Radovanovi\u0107","year":"2014","unstructured":"Radovanovi\u0107, M., Nanopoulos, A., Ivanovi\u0107, M.: Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE Trans. Knowl. Data Eng. 27(5), 1369\u20131382 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"14_CR42","doi-asserted-by":"crossref","unstructured":"Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 427\u2013438 (2000)","DOI":"10.1145\/335191.335437"},{"key":"14_CR43","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001\u20132010 (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"14_CR44","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-3-030-63799-6_5","volume-title":"Artificial Intelligence XXXVII","author":"R Saha","year":"2020","unstructured":"Saha, R., Granmo, O.-C., Goodwin, M.: Mining interpretable rules for sentiment and semantic relation analysis using tsetlin machines. In: Bramer, M., Ellis, R. (eds.) SGAI 2020. LNCS (LNAI), vol. 12498, pp. 67\u201378. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63799-6_5"},{"issue":"12","key":"14_CR45","doi-asserted-by":"publisher","first-page":"3246","DOI":"10.1109\/TKDE.2016.2597833","volume":"28","author":"M Salehi","year":"2016","unstructured":"Salehi, M., Leckie, C., Bezdek, J.C., Vaithianathan, T., Zhang, X.: Fast memory efficient local outlier detection in data streams. IEEE Trans. Knowl. Data Eng. 28(12), 3246\u20133260 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7","key":"14_CR46","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","volume":"35","author":"WJ Scheirer","year":"2012","unstructured":"Scheirer, W.J., de Rezende Rocha, A., Sapkota, A., Boult, T.E.: Toward open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1757\u20131772 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR47","doi-asserted-by":"publisher","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T., Shen, D. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12","DOI":"10.1007\/978-3-319-59050-9_12"},{"issue":"7","key":"14_CR48","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001)","journal-title":"Neural Comput."},{"key":"14_CR49","unstructured":"Tang, J., Ngan, H.Y.: Traffic outlier detection by density-based bounded local outlier factors. Inf. Technol. Ind. 4(1) (2016)"},{"key":"14_CR50","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/3-540-47887-6_53","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"J Tang","year":"2002","unstructured":"Tang, J., Chen, Z., Fu, A.W., Cheung, D.W.: Enhancing effectiveness of outlier detections for low density patterns. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 535\u2013548. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-47887-6_53"},{"issue":"1","key":"14_CR51","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DM Tax","year":"2004","unstructured":"Tax, D.M., Duin, R.P.: Support vector data description. Mach. Learn. 54(1), 45\u201366 (2004)","journal-title":"Mach. Learn."},{"key":"14_CR52","unstructured":"Yadav, R.K., Jiao, L., Granmo, O.C., Goodwin, M.: Distributed word representation in Tsetlin Machine. arXiv preprint arXiv:2104.06901 (2021)"},{"key":"14_CR53","doi-asserted-by":"crossref","unstructured":"Yadav, R.K., Jiao, L., Granmo, O.C., Goodwin, M.: Human-level interpretable learning for aspect-based sentiment analysis. In: The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) (2021)","DOI":"10.1609\/aaai.v35i16.17671"},{"issue":"1","key":"14_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4108\/trans.sis.2013.01-03.e1","volume":"13","author":"J Zhang","year":"2013","unstructured":"Zhang, J.: Advancements of outlier detection: a survey. ICST Trans. Scalable Inf. Syst. 13(1), 1\u201326 (2013)","journal-title":"ICST Trans. Scalable Inf. Syst."},{"key":"14_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, L., et al.: Probabilistic-mismatch anomaly detection: do one\u2019s medications match with the diagnoses. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 659\u2013668. IEEE (2016)","DOI":"10.1109\/ICDM.2016.0077"},{"key":"14_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, X., Jiao, L., Granmo, O.C., Goodwin, M.: On the convergence of tsetlin machines for the identity-and not operators. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)","DOI":"10.1109\/TPAMI.2021.3085591"},{"issue":"2","key":"14_CR57","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1109\/SURV.2010.021510.00088","volume":"12","author":"Y Zhang","year":"2010","unstructured":"Zhang, Y., Meratnia, N., Havinga, P.: Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun. Surv. Tutorials 12(2), 159\u2013170 (2010)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"14_CR58","first-page":"1","volume":"20","author":"Y Zhao","year":"2019","unstructured":"Zhao, Y., Nasrullah, Z., Li, Z.: Pyod: a python toolbox for scalable outlier detection. J. Mach. Learn. Res. 20, 1\u20137 (2019)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Agents and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-10161-8_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T18:27:37Z","timestamp":1700850457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-10161-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031101601","9783031101618"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-10161-8_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Agents and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 February 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 February 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaart2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icaart.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"298","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 selected papers are included in the LNAI proceedings","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}