{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:36:13Z","timestamp":1742913373599,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031703409"},{"type":"electronic","value":"9783031703416"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70341-6_7","type":"book-chapter","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T20:26:39Z","timestamp":1725049599000},"page":"107-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AEMLO: AutoEncoder-Guided Multi-label Oversampling"],"prefix":"10.1007","author":[{"given":"Ao","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Kaiwei","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Kelin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"7_CR1","unstructured":"Andrew, G., Arora, R., Bilmes, J., Livescu, K.: Deep canonical correlation analysis. In: International Conference on Machine Learning, pp. 1247\u20131255. PMLR (2013)"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10994-017-5670-4","volume":"107","author":"C Bellinger","year":"2018","unstructured":"Bellinger, C., Drummond, C., Japkowicz, N.: Manifold-based synthetic oversampling with manifold conformance estimation. Mach. Learn. 107, 605\u2013637 (2018)","journal-title":"Mach. Learn."},{"issue":"9","key":"7_CR3","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757\u20131771 (2004)","journal-title":"Pattern Recogn."},{"key":"7_CR4","unstructured":"Cabral, R., Torre, F., Costeira, J.P., Bernardino, A.: Matrix completion for multi-label image classification. In: Advances in Neural Information Processing Systems, vol. 24 (2011)"},{"key":"7_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/978-3-319-19644-2_41","volume-title":"Hybrid Artificial Intelligent Systems","author":"F Charte","year":"2015","unstructured":"Charte, F., Rivera, A., del Jesus, M.J., Herrera, F.: Resampling multilabel datasets by decoupling highly imbalanced labels. In: Onieva, E., Santos, I., Osaba, E., Quinti\u00e1n, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 489\u2013501. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-19644-2_41"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.knosys.2015.07.019","volume":"89","author":"F Charte","year":"2015","unstructured":"Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: Mlsmote: approaching imbalanced multilabel learning through synthetic instance generation. Knowl.-Based Syst. 89, 385\u2013397 (2015)","journal-title":"Knowl.-Based Syst."},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.neucom.2017.01.118","volume":"326","author":"F Charte","year":"2019","unstructured":"Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: REMEDIAL-HwR: tackling multilabel imbalance through label decoupling and data resampling hybridization. Neurocomputing 326, 110\u2013122 (2019)","journal-title":"Neurocomputing"},{"issue":"9","key":"7_CR8","doi-asserted-by":"publisher","first-page":"6390","DOI":"10.1109\/TNNLS.2021.3136503","volume":"34","author":"D Dablain","year":"2022","unstructured":"Dablain, D., Krawczyk, B., Chawla, N.V.: Deepsmote: fusing deep learning and smote for imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 34(9), 6390\u20136404 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Daniels, Z., Metaxas, D.: Addressing imbalance in multi-label classification using structured hellinger forests. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.10908"},{"key":"7_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114463","volume":"169","author":"VA Fajardo","year":"2021","unstructured":"Fajardo, V.A., et al.: On oversampling imbalanced data with deep conditional generative models. Expert Syst. Appl. 169, 114463 (2021)","journal-title":"Expert Syst. Appl."},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10994-008-5064-8","volume":"73","author":"J F\u00fcrnkranz","year":"2008","unstructured":"F\u00fcrnkranz, J., H\u00fcllermeier, E., Loza Menc\u00eda, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73, 133\u2013153 (2008)","journal-title":"Mach. Learn."},{"key":"7_CR12","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504\u2013507 (2006)","DOI":"10.1126\/science.1127647"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, T., Wang, D., Sun, L., Yang, H., Zhao, Z., Zhuang, F.: Lightxml: transformer with dynamic negative sampling for high-performance extreme multi-label text classification. In: AAAI, pp. 7987\u20137994 (2021)","DOI":"10.1609\/aaai.v35i9.16974"},{"key":"7_CR15","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Liang, J., Phan, H., Benetos, E.: Learning from taxonomy: multi-label few-shot classification for everyday sound recognition. In: ICASSP, pp. 771\u2013775. IEEE (2024)","DOI":"10.1109\/ICASSP48485.2024.10446908"},{"key":"7_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108294","volume":"122","author":"B Liu","year":"2022","unstructured":"Liu, B., Blekas, K., Tsoumakas, G.: Multi-label sampling based on local label imbalance. Pattern Recogn. 122, 108294 (2022)","journal-title":"Pattern Recogn."},{"key":"7_CR18","unstructured":"Liu, B., Tsoumakas, G.: Making classifier chains resilient to class imbalance. In: Asian Conference on Machine Learning, pp. 280\u2013295. PMLR (2018)"},{"key":"7_CR19","unstructured":"Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., Malossi, C.: Bagan: data augmentation with balancing GAN. In: International Conference on Machine Learning (2018)"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Mullick, S.S., Datta, S., Das, S.: Generative adversarial minority oversampling. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1695\u20131704 (2019)","DOI":"10.1109\/ICCV.2019.00178"},{"key":"7_CR21","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neucom.2019.11.076","volume":"383","author":"RM Pereira","year":"2020","unstructured":"Pereira, R.M., Costa, Y.M., Silla, C.N., Jr.: MLTL: a multi-label approach for the tomek link undersampling algorithm. Neurocomputing 383, 95\u2013105 (2020)","journal-title":"Neurocomputing"},{"key":"7_CR22","unstructured":"Razavi, A., Van\u00a0den Oord, A., Vinyals, O.: Generating diverse high-fidelity images with VQ-VAE-2. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"7_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/978-3-642-04174-7_17","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"J Read","year":"2009","unstructured":"Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Buntine, W., Grobelnik, M., Mladeni\u0107, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 254\u2013269. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04174-7_17"},{"key":"7_CR24","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85, 333\u2013359 (2011)","journal-title":"Mach. Learn."},{"key":"7_CR25","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2014.08.091","volume":"163","author":"F Charte","year":"2015","unstructured":"Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing 163, 3\u201316 (2015)","journal-title":"Neurocomputing"},{"key":"7_CR26","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-3-642-23808-6_10","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"K Sechidis","year":"2011","unstructured":"Sechidis, K., Tsoumakas, G., Vlahavas, I.: On the stratification of multi-label data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6913, pp. 145\u2013158. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23808-6_10"},{"issue":"5","key":"7_CR27","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/j.patrec.2011.10.019","volume":"33","author":"MA Tahir","year":"2012","unstructured":"Tahir, M.A., Kittler, J., Bouridane, A.: Multilabel classification using heterogeneous ensemble of multi-label classifiers. Pattern Recogn. Lett. 33(5), 513\u2013523 (2012)","journal-title":"Pattern Recogn. Lett."},{"key":"7_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107965","volume":"118","author":"AN Tarekegn","year":"2021","unstructured":"Tarekegn, A.N., Giacobini, M., Michalak, K.: A review of methods for imbalanced multi-label classification. Pattern Recogn. 118, 107965 (2021)","journal-title":"Pattern Recogn."},{"key":"7_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109953","volume":"145","author":"Z Teng","year":"2024","unstructured":"Teng, Z., Cao, P., Huang, M., Gao, Z., Wang, X.: Multi-label borderline oversampling technique. Pattern Recogn. 145, 109953 (2024)","journal-title":"Pattern Recogn."},{"key":"7_CR30","first-page":"2411","volume":"12","author":"G Tsoumakas","year":"2011","unstructured":"Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: Mulan: a java library for multi-label learning. J. Mach. Learn. Res. 12, 2411\u20132414 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/978-3-540-74958-5_38","volume-title":"Machine Learning: ECML 2007","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladeni\u010d, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406\u2013417. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-74958-5_38"},{"issue":"6","key":"7_CR32","doi-asserted-by":"publisher","first-page":"4459","DOI":"10.1109\/TCYB.2020.3027509","volume":"52","author":"ML Zhang","year":"2020","unstructured":"Zhang, M.L., Li, Y.K., Yang, H., Liu, X.Y.: Towards class-imbalance aware multi-label learning. IEEE Trans. Cybern. 52(6), 4459\u20134471 (2020)","journal-title":"IEEE Trans. Cybern."},{"key":"7_CR33","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s11063-009-9095-3","volume":"29","author":"ML Zhang","year":"2009","unstructured":"Zhang, M.L.: ML-rbf: RBF neural networks for multi-label learning. Neural Process. Lett. 29, 61\u201374 (2009)","journal-title":"Neural Process. Lett."},{"issue":"10","key":"7_CR34","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","volume":"18","author":"ML Zhang","year":"2006","unstructured":"Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338\u20131351 (2006)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7","key":"7_CR35","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."},{"issue":"8","key":"7_CR36","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"ML Zhang","year":"2013","unstructured":"Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819\u20131837 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"7_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, B., Pan, X., vanden Broucke, S., Xiao, J.: A GAN-based hybrid sampling method for imbalanced customer classification. Inf. Sci. 609, 1397\u20131411 (2022)","DOI":"10.1016\/j.ins.2022.07.145"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70341-6_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T20:27:53Z","timestamp":1725049673000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70341-6_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703409","9783031703416"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70341-6_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","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":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","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":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}