{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:25:20Z","timestamp":1742999120659,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031157905"},{"type":"electronic","value":"9783031157912"}],"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\/978-3-031-15791-2_5","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T07:34:06Z","timestamp":1663313646000},"page":"45-52","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the\u00a0Performance Gain on\u00a0Retail Article Categorization at\u00a0the\u00a0Expense of\u00a0Explainability and\u00a0Resource Efficiency"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1235-700X","authenticated-orcid":false,"given":"Eduardo","family":"Brito","sequence":"first","affiliation":[]},{"given":"Vishwani","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Hahn","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2691-1406","authenticated-orcid":false,"given":"Sven","family":"Giesselbach","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"5_CR1","unstructured":"Bhatia, K., et al.: The extreme classification repository: multi-label datasets and code (2016). http:\/\/manikvarma.org\/downloads\/XC\/XMLRepository.html"},{"key":"5_CR2","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135\u2013146 (2017). https:\/\/doi.org\/10.1162\/tacl_a_00051, https:\/\/aclanthology.org\/Q17-1010","DOI":"10.1162\/tacl_a_00051"},{"key":"5_CR4","unstructured":"Brito, E., Georgiev, B., Domingo-Fern\u00e1ndez, D., Hoyt, C.T., Bauckhage, C.: Ratvec: a general approach for low-dimensional distributed vector representations via rational kernels. In: LWDA, pp. 74\u201378 (2019)"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Gallagher, R.J., Reing, K., Kale, D., Ver\u00a0Steeg, G.: Anchored correlation explanation: Topic modeling with minimal domain knowledge. Trans. Assoc. Comput. Linguist. 5, 529\u2013542 (2017). https:\/\/doi.org\/10.1162\/tacl_a_00078, https:\/\/aclanthology.org\/Q17-1037","DOI":"10.1162\/tacl_a_00078"},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jpdc.2019.07.007","volume":"134","author":"E Garc\u00eda-Mart\u00edn","year":"2019","unstructured":"Garc\u00eda-Mart\u00edn, E., Rodrigues, C.F., Riley, G., Grahn, H.: Estimation of energy consumption in machine learning. J. Parallel Distrib. Comput. 134, 75\u201388 (2019)","journal-title":"J. Parallel Distrib. Comput."},{"key":"5_CR7","unstructured":"Hong, D., Baek, S.S., Wang, T.: Interpretable sequence classification via prototype trajectory (2021)"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: spaCy: industrial-strength natural language processing in python (2020). https:\/\/doi.org\/10.5281\/zenodo.1212303","DOI":"10.5281\/zenodo.1212303"},{"key":"5_CR9","unstructured":"Jagarlamudi, J., Daum\u00e9 III, H., Udupa, R.: Incorporating lexical priors into topic models. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 204\u2013213. Association for Computational Linguistics, Avignon (2012). http:\/\/aclanthology.org\/E12-1021"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Jain, H., Prabhu, Y., Varma, M.: Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 935\u2013944. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939756","DOI":"10.1145\/2939672.2939756"},{"key":"5_CR11","unstructured":"Molnar, C.: Interpretable machine learning (2020). http:\/\/christophm.github.io\/interpretable-ml-book\/"},{"key":"5_CR12","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-030-93736-2_35","volume-title":"Machine Learning and Principles and Practice of Knowledge Discovery in Databases","author":"K Pluci\u0144ski","year":"2021","unstructured":"Pluci\u0144ski, K., Lango, M., Stefanowski, J.: Prototypical convolutional neural network for a phrase-based explanation of sentiment classification. In: Kamp, M., et al. (eds.) ECML PKDD 2021. Communications in Computer and Information Science, vol. 1524, pp. 457\u2013472. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93736-2_35"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982\u20133992. Association for Computational Linguistics, Hong Kong (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1410, http:\/\/aclanthology.org\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"issue":"5","key":"5_CR14","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206\u2013215 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"5_CR15","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter (2020)"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3645\u20133650. Association for Computational Linguistics, Florence (2019). https:\/\/doi.org\/10.18653\/v1\/P19-1355, http:\/\/aclanthology.org\/P19-1355","DOI":"10.18653\/v1\/P19-1355"},{"key":"5_CR17","unstructured":"Szyma\u0144ski, P., Kajdanowicz, T.: A scikit-based Python environment for performing multi-label classification. ArXiv e-prints (2017)"},{"key":"5_CR18","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"7","key":"5_CR19","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038\u20132048 (2007)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","KI 2022: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15791-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T07:35:07Z","timestamp":1663313707000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15791-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031157905","9783031157912"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15791-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"12 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"German Conference on Artificial Intelligence (K\u00fcnstliche Intelligenz)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Trier","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"45","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ki2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ki2022.gi.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47","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":"12","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":"5","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":"26% - 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":"2,6","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":"2,1","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":"Due to COVID-19 the conference was held virtually","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)"}}]}}