{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:46:54Z","timestamp":1743007614672,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030185756"},{"type":"electronic","value":"9783030185763"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-18576-3_6","type":"book-chapter","created":{"date-parts":[[2019,4,23]],"date-time":"2019-04-23T08:05:29Z","timestamp":1556006729000},"page":"87-103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PU-Shapelets: Towards Pattern-Based Positive Unlabeled Classification of Time Series"],"prefix":"10.1007","author":[{"given":"Shen","family":"Liang","sequence":"first","affiliation":[]},{"given":"Yanchun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jiangang","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,24]]},"reference":[{"key":"6_CR1","unstructured":"PU-Shapelets source code. https:\/\/github.com\/sliang11\/PU-Shapelets"},{"issue":"3","key":"6_CR2","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2017","unstructured":"Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3), 606\u2013660 (2017)","journal-title":"Data Min. Knowl. Discov."},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Begum, N., Hu, B., Rakthanmanon, T., Keogh, E.: Towards a minimum description length based stopping criterion for semi-supervised time series classification. In: 2013 IEEE 14th International Conference on Information Reuse Integration, pp. 333\u2013340 (2013)","DOI":"10.1109\/IRI.2013.6642490"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Hu, B., Keogh, E., Batista, G.: DTW-D: time series semi-supervised learning from a single example. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 383\u2013391 (2013)","DOI":"10.1145\/2487575.2487633"},{"key":"6_CR5","unstructured":"Chen, Y., et al.: The UCR time series classification archive, July 2015. www.cs.ucr.edu\/~eamonn\/time_series_data\/"},{"key":"6_CR6","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.ins.2015.07.061","volume":"328","author":"M Gonz\u00e1lez","year":"2016","unstructured":"Gonz\u00e1lez, M., Bergmeir, C., Triguero, I., Rodr\u00edguez, Y., Ben\u00edtez, J.: On the stopping criteria for k-nearest neighbor in positive unlabeled time series classification problems. Inf. Sci. 328, 42\u201359 (2016)","journal-title":"Inf. Sci."},{"issue":"4","key":"6_CR7","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/s10618-013-0322-1","volume":"28","author":"J Hills","year":"2014","unstructured":"Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Min. Knowl. Discov. 28(4), 851\u2013881 (2014)","journal-title":"Data Min. Knowl. Discov."},{"key":"6_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1007\/11564096_24","volume-title":"Machine Learning: ECML 2005","author":"X-L Li","year":"2005","unstructured":"Li, X.-L., Liu, B.: Learning from positive and unlabeled examples with different data distributions. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 218\u2013229. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564096_24"},{"issue":"1","key":"6_CR9","doi-asserted-by":"publisher","first-page":"4:1","DOI":"10.1145\/2806890","volume":"16","author":"J Ma","year":"2016","unstructured":"Ma, J., Sun, L., Wang, H., Zhang, Y., Aickelin, W.: Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. 16(1), 4:1\u20134:20 (2016)","journal-title":"ACM Trans. Internet Technol."},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Mueen, A., Keogh, E., Young, N.: Logical-shapelets: an expressive primitive for time series classification. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1154\u20131162 (2011)","DOI":"10.1145\/2020408.2020587"},{"key":"6_CR11","unstructured":"Nguyen, M.N., Li, X., Ng, S.: Positive unlabeled learning for time series classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 1421\u20131426 (2011)"},{"key":"6_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-642-29038-1_19","volume-title":"Database Systems for Advanced Applications","author":"MN Nguyen","year":"2012","unstructured":"Nguyen, M.N., Li, X.-L., Ng, S.-K.: Ensemble based positive unlabeled learning for time series classification. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 243\u2013257. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-29038-1_19"},{"key":"6_CR13","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-540-70560-4_1","volume-title":"Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing","author":"CA Ratanamahatana","year":"2008","unstructured":"Ratanamahatana, C.A., Wanichsan, D.: Stopping criterion selection for efficient semi-supervised time series classification. In: Lee, R. (ed.) Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing. SCI, vol. 149, pp. 1\u201314. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-70560-4_1"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Sart, D., Mueen, A., Najjar, W., Keogh, E., Niennattrakul, V.: Accelerating dynamic time warping subsequence search with GPUs and FPGAs. In: 2010 IEEE 10th International Conference on Data Mining, pp. 1001\u20131006 (2010)","DOI":"10.1109\/ICDM.2010.21"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Ulanova, L., Begum, N., Keogh, E.: Scalable clustering of time series with U-shapelets. In: Proceedings of the 2015 SIAM International Conference on Data Mining, pp. 900\u2013908 (2015)","DOI":"10.1137\/1.9781611974010.101"},{"key":"6_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/978-3-662-53580-6_8","volume-title":"Transactions on Computational Collective Intelligence XXV","author":"VT Vinh","year":"2016","unstructured":"Vinh, V.T., Anh, D.T.: Two novel techniques to improve MDL-based semi-supervised classification of time series. In: Nguyen, N.T., Kowalczyk, R., Or\u0142owski, C., Zi\u00f3\u0142kowski, A. (eds.) Transactions on Computational Collective Intelligence XXV. LNCS, vol. 9990, pp. 127\u2013147. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-53580-6_8"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Wei, L., Keogh, E.: Semi-supervised time series classification. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 748\u2013753 (2006)","DOI":"10.1145\/1150402.1150498"},{"issue":"1\u20132","key":"6_CR18","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10618-010-0179-5","volume":"22","author":"L Ye","year":"2011","unstructured":"Ye, L., Keogh, E.: Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min. Knowl. Discov. 22(1\u20132), 149\u2013182 (2011)","journal-title":"Data Min. Knowl. Discov."},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Zakaria, J., Mueen, A., Keogh, E.: Clustering time series using unsupervised-shapelets. In: 2012 IEEE 12th International Conference on Data Mining, pp. 785\u2013794 (2012)","DOI":"10.1109\/ICDM.2012.26"},{"issue":"4","key":"6_CR20","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1007\/s11390-015-1565-7","volume":"30","author":"J Zhou","year":"2015","unstructured":"Zhou, J., Zhu, S., Huang, X., Zhang, Y.: Enhancing time series clustering by incorporating multiple distance measures with semi-supervised learning. J. Comput. Sci. Technol. 30(4), 859\u2013873 (2015)","journal-title":"J. Comput. Sci. Technol."},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. In: Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 3, no. 1, pp. 1\u2013130 (2009)","DOI":"10.2200\/S00196ED1V01Y200906AIM006"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-18576-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:10:31Z","timestamp":1710335431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-18576-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030185756","9783030185763"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-18576-3_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chiang Mai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2019.eng.cmu.ac.th\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"501","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":"92","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":"64","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":"18% - 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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13 demo papers, 6 tutorial papers","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)"}}]}}