{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T21:01:02Z","timestamp":1765486862555,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031236174"},{"type":"electronic","value":"9783031236181"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-23618-1_25","type":"book-chapter","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:05:49Z","timestamp":1675062349000},"page":"369-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Local Multi-label Explanations for\u00a0Random Forest"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5733-543X","authenticated-orcid":false,"given":"Nikolaos","family":"Mylonas","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7765-7903","authenticated-orcid":false,"given":"Ioannis","family":"Mollas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6035-1038","authenticated-orcid":false,"given":"Nick","family":"Bassiliades","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7879-669X","authenticated-orcid":false,"given":"Grigorios","family":"Tsoumakas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018)","journal-title":"IEEE Access"},{"key":"25_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/978-3-540-48247-5_4","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"H Blockeel","year":"1999","unstructured":"Blockeel, H., D\u017eeroski, S., Grbovi\u0107, J.: Simultaneous prediction of multiple chemical parameters of river water quality with TILDE. In: \u017bytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 32\u201340. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/978-3-540-48247-5_4"},{"issue":"2","key":"25_CR3","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/j.ejor.2019.05.037","volume":"279","author":"M Bogaert","year":"2019","unstructured":"Bogaert, M., Lootens, J., Van den Poel, D., Ballings, M.: Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur. J. Oper. Res. 279(2), 620\u2013634 (2019)","journal-title":"Eur. J. Oper. Res."},{"issue":"1","key":"25_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"25_CR5","unstructured":"Craven, M., Shavlik, J.: Extracting tree-structured representations of trained networks. In: Touretzky, D., Mozer, M.C., Hasselmo, M. (eds.), Advances in Neural Information Processing Systems, vol. 8. MIT Press, Cambridge (1995)"},{"issue":"4","key":"25_CR6","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s41060-018-0144-8","volume":"7","author":"H Deng","year":"2018","unstructured":"Deng, H.: Interpreting tree ensembles with inTrees. Int. J. Data Sci. Anal. 7(4), 277\u2013287 (2018). https:\/\/doi.org\/10.1007\/s41060-018-0144-8","journal-title":"Int. J. Data Sci. Anal."},{"key":"25_CR7","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017)"},{"issue":"1","key":"25_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2594473.2594475","volume":"15","author":"AA Freitas","year":"2014","unstructured":"Freitas, A.A.: Comprehensible classification models: a position paper. SIGKDD Explor. Newsl. 15(1), 1\u201310 (2014)","journal-title":"SIGKDD Explor. Newsl."},{"issue":"3","key":"25_CR9","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1214\/07-AOAS148","volume":"2","author":"JH Friedman","year":"2008","unstructured":"Friedman, J.H., Popescu, B.E.: Predictive learning via rule ensembles. Ann. Appl. Stat. 2(3), 916\u2013954 (2008)","journal-title":"Ann. Appl. Stat."},{"key":"25_CR10","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.neucom.2019.08.089","volume":"370","author":"T Gong","year":"2019","unstructured":"Gong, T., Liu, B., Chu, Q., Nenghai, Yu.: Using multi-label classification to improve object detection. Neurocomputing 370, 174\u2013185 (2019)","journal-title":"Neurocomputing"},{"issue":"6","key":"25_CR11","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MIS.2019.2957223","volume":"34","author":"R Guidotti","year":"2019","unstructured":"Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., Turini, F.: Factual and counterfactual explanations for black box decision making. IEEE Intell. Syst. 34(6), 14\u201323 (2019)","journal-title":"IEEE Intell. Syst."},{"issue":"5","key":"25_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 1\u201342 (2018)","journal-title":"ACM Comput. Surv."},{"key":"25_CR13","unstructured":"Hara, S., Hayashi, K.: Making tree ensembles interpretable: a bayesian model selection approach. In: Storkey, A., Perez-Cruz, F. (eds.), Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, volume 84 of Proceedings of Machine Learning Research, pp. 77\u201385. PMLR, 09\u201311 April 2018"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Hatwell, J., Gaber, M.M., Muhammad Atif Azad, R.: CHIRPS: explaining random forest classification. Artif. Intell. Rev. 53(8), 5747\u20135788 (2020)","DOI":"10.1007\/s10462-020-09833-6"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Samaneh Kouchaki, Yang Yang, Alexander Lachapelle, Timothy M. Walker, A. Sarah Walker, CRyPTIC Consortium, Timothy E. A. Peto, Derrick W. Crook, and David A. Clifton. Multi-label random forest model for tuberculosis drug resistance classification and mutation ranking. Frontiers in Microbiology, 11, 2020","DOI":"10.3389\/fmicb.2020.00667"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Matzka, S.: Explainable artificial intelligence for predictive maintenance applications. In: 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), pp. 69\u201374. IEEE (2020)","DOI":"10.1109\/AI4I49448.2020.00023"},{"key":"25_CR17","doi-asserted-by":"publisher","unstructured":"Mollas, I., Bassiliades, N., Tsoumakas, G.: Conclusive local interpretation rules for random forests. Data Min. Knowl. Disc 36, 1521\u20131574 (2022). https:\/\/doi.org\/10.1007\/s10618-022-00839-y","DOI":"10.1007\/s10618-022-00839-y"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Moore, A., Murdock, V., Cai, Y., Jones, K.: Transparent tree ensembles. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, pp. 1241\u20131244, New York, NY, USA, Association for Computing Machinery (2018)","DOI":"10.1145\/3209978.3210151"},{"issue":"2","key":"25_CR19","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/TVCG.2020.3030354","volume":"27","author":"MP Neto","year":"2021","unstructured":"Neto, M.P., Paulovich, F.V.: Explainable matrix - visualization for global and local interpretability of random forest classification ensembles. IEEE Trans. Vis. Comput. Graph. 27(2), 1427\u20131437 (2021)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"25_CR20","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-030-24409-5_9","volume-title":"Precision Health and Medicine","author":"C Panigutti","year":"2020","unstructured":"Panigutti, C., Guidotti, R., Monreale, A., Pedreschi, D.: Explaining multi-label black-box classifiers for health applications. In: Shaban-Nejad, A., Michalowski, M. (eds.) W3PHAI 2019. SCI, vol. 843, pp. 97\u2013110. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-24409-5_9"},{"key":"25_CR21","doi-asserted-by":"publisher","unstructured":"Papanikolaou, Y., Tsoumakas, G., Laliotis, M., Markantonatos, N., Vlahavas, I.: Large-scale online semantic indexing of biomedical articles via an ensemble of multi-label classification models. J. Biomed. Semant. 8(1), 43:1\u201343:13 (2017). https:\/\/doi.org\/10.1186\/s13326-017-0150-0","DOI":"10.1186\/s13326-017-0150-0"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"25_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/978-3-319-65340-2_48","volume-title":"Progress in Artificial Intelligence","author":"A Rivolli","year":"2017","unstructured":"Rivolli, A., Parker, L.C., de Carvalho, A.C.P.L.F.: Food truck recommendation using multi-label classification. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds.) EPIA 2017. LNCS (LNAI), vol. 10423, pp. 585\u2013596. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-65340-2_48"},{"issue":"16","key":"25_CR25","doi-asserted-by":"publisher","first-page":"7507","DOI":"10.1016\/j.eswa.2014.06.015","volume":"41","author":"L Rokach","year":"2014","unstructured":"Rokach, L., Schclar, A., Itach, E.: Ensemble methods for multi-label classification. Expert Syst. Appl. 41(16), 7507\u20137523 (2014)","journal-title":"Expert Syst. Appl."},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Sharma, S., Mehrotra, D.: Comparative analysis of multi-label classification algorithms. In: 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), pp. 35\u201338 (2018)","DOI":"10.1109\/ICSCCC.2018.8703285"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Tabia, K.: Towards explainable multi-label classification. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1088\u20131095 (2019)","DOI":"10.1109\/ICTAI.2019.00152"},{"issue":"3","key":"25_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2007070101","volume":"3","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1\u201313 (2007)","journal-title":"Int. J. Data Warehous. Min."},{"issue":"10","key":"25_CR29","doi-asserted-by":"publisher","first-page":"2665","DOI":"10.1109\/TKDE.2016.2581161","volume":"28","author":"W Qingyao","year":"2016","unstructured":"Qingyao, W., Tan, M., Song, H., Chen, J., Michael, K.N.: Ml-forest: a multi-label tree ensemble method for multi-label classification. IEEE Trans. Knowl. Data Eng. 28(10), 2665\u20132680 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"6","key":"25_CR30","doi-asserted-by":"publisher","first-page":"337","DOI":"10.3390\/pr7060337","volume":"7","author":"X Wu","year":"2019","unstructured":"Wu, X., Gao, Y., Jiao, D.: Multi-label classification based on random forest algorithm for non-intrusive load monitoring system. Processes 7(6), 337 (2019)","journal-title":"Processes"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Zhao, X., Wu, Y., Lee, D.L., Cui, W.: iforest: interpreting random forests via visual analytics. IEEE Trans. Vis. Comput. Graph. 25(1), 407\u2013416 (2019)","DOI":"10.1109\/TVCG.2018.2864475"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23618-1_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:13:37Z","timestamp":1675062817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23618-1_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031236174","9783031236181"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23618-1_25","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","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":"236","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":"0","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":"22% - 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":"3-4","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":"3-4","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 demo track papers have been accepted from 28 submissions","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)"}}]}}