{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:37:43Z","timestamp":1742913463161,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819756650"},{"type":"electronic","value":"9789819756667"}],"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\/978-981-97-5666-7_34","type":"book-chapter","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T20:37:45Z","timestamp":1722544665000},"page":"404-415","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Weight Distribution Method of Loss Function Based on Category Theory"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9162-1500","authenticated-orcid":false,"given":"Jiehao","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8505-6649","authenticated-orcid":false,"given":"Heng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"34_CR1","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: MOVNG: applied a novel sparse fusion representation into GTCN for pan-cancer classification and biomarker identification. In: International Conference on Intelligent Computing (2023)","DOI":"10.1007\/978-981-99-4755-3_52"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Identification of suitable technologies for drinking water quality prediction: a comparative study of traditional, ensemble, cost-sensitive, outlier detection learning models and sampling algorithms. In: ACS ES&T Water (2021)","DOI":"10.1021\/acsestwater.1c00037"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Desiani, A., et al.: Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment. IAES Int. J. Artif. Intell. 10, 346 (IJ-AI) (2021)","DOI":"10.11591\/ijai.v10.i2.pp346-354"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hui, L.: Rolling bearing fault diagnosis based on graph convolution neural network. In: International Conference on Intelligent Computing (2022)","DOI":"10.1007\/978-3-031-13870-6_16"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Oksuz, K., et al.: Imbalance problems in object detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. 43, 3388\u20133415 (2019)","DOI":"10.1109\/TPAMI.2020.2981890"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Mathews, L., Seetha, H.: Learning from imbalanced data. In: Advances in Computer and Electrical Engineering (2019)","DOI":"10.4018\/978-1-5225-7598-6.ch030"},{"key":"34_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-018-0151-6","volume":"5","author":"JL Leevy","year":"2018","unstructured":"Leevy, J.L., et al.: A survey on addressing high-class imbalance in big data. J. Big Data 5, 1\u201330 (2018)","journal-title":"J. Big Data"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Rend\u00f3n, E., et al.: Data sampling methods to deal with the big data multi-class imbalance problem. Appl. Sci. 10, 1276 (2020)","DOI":"10.3390\/app10041276"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Peng, M., et al.: Trainable undersampling for class-imbalance learning. In: AAAI Conference on Artificial Intelligence (2019)","DOI":"10.1609\/aaai.v33i01.33014707"},{"key":"34_CR10","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","volume":"505","author":"D Elreedy","year":"2019","unstructured":"Elreedy, D., Atiya, A.F.: A comprehensive analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance. Inf. Sci. 505, 32\u201364 (2019)","journal-title":"Inf. Sci."},{"key":"34_CR11","unstructured":"Yang, Y., et al.: Delving into deep imbalanced regression. In: International Conference on Machine Learning. PMLR (2021)"},{"key":"34_CR12","first-page":"1513","volume":"33","author":"K Tang","year":"2020","unstructured":"Tang, K., Huang, J., Zhang, H.: Long-tailed classification by keeping the good and removing the bad momentum causal effect. Adv. Neural. Inf. Process. Syst. 33, 1513\u20131524 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Alshammari, S., et al.: Long- Tailed Recognition via Weight Balancing. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6887\u20136897 (2022)","DOI":"10.1109\/CVPR52688.2022.00677"},{"key":"34_CR14","unstructured":"Spivak, D.I.: Basic Category Theory (2014)"},{"key":"34_CR15","unstructured":"Shiebler, D., et al.: Category theory in machine learning. ArXiv abs\/2106.07032 (2021)"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Wilson, P.W., Fabio, Z.: Reverse derivative ascent: a categorical approach to learning Boolean circuits. ACT (2021)","DOI":"10.4204\/EPTCS.333.17"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Cruttwell, G.S.H., et al.: Categorical foundations of gradient-based learning. In: European Symposium on Programming (2021)","DOI":"10.1007\/978-3-030-99336-8_1"},{"key":"34_CR18","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.3390\/e21121234","volume":"21","author":"G Northoff","year":"2019","unstructured":"Northoff, G., et al.: Mathematics and the brain: a category theoretical approach to go beyond the neural correlates of consciousness. Entropy 21, 1234 (2019)","journal-title":"Entropy"},{"key":"34_CR19","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1109\/TPAMI.2018.2832629","volume":"41","author":"Q Dong","year":"2018","unstructured":"Dong, Q., et al.: Imbalanced deep learning by minority class incremental rectification. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1367\u20131381 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR20","unstructured":"Cao, K., et al.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., et al.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999\u20133007 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"34_CR22","unstructured":"Kini, G.R., et al.: Label-Imbalanced and group-sensitive classification under over parameterization. In: Advances in Neural Information Processing Systems (2021)"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: A dynamic resampling based intrusion detection method. In: International Conference on Intelligent Computing (2023)","DOI":"10.1007\/978-981-99-4755-3_39"},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Park, S., et al.: The majority can help the minority: context-rich minority oversampling for long-tailed classification. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6877\u20136886 (2021)","DOI":"10.1109\/CVPR52688.2022.00676"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Sambasivam, G., Geoffrey, D.O.: A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian Informatics J. (2020)","DOI":"10.1016\/j.eij.2020.02.007"},{"key":"34_CR26","doi-asserted-by":"crossref","unstructured":"Cui, Y., et al.: Class-balanced loss based on effective number of samples. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9260\u20139269 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"34_CR27","doi-asserted-by":"publisher","unstructured":"Thiry, L., et al.: Categories for (Big) data models and optimization. J. Big Data 5, 21 (2018). https:\/\/doi.org\/10.1186\/s40537-018-0132-9","DOI":"10.1186\/s40537-018-0132-9"},{"key":"34_CR28","first-page":"104213","volume":"197","author":"M Fuyama","year":"2020","unstructured":"Fuyama, M., et al.: A category theoretic approach to metaphor comprehension: theory of indeterminate natural transformation. Bio Syst. 197, 104213 (2020)","journal-title":"Bio Syst."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5666-7_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T20:48:35Z","timestamp":1722545315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5666-7_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756650","9789819756667"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5666-7_34","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":"1 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}