{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:02:30Z","timestamp":1757628150520,"version":"3.44.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032028129"},{"type":"electronic","value":"9783032028136"}],"license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-02813-6_10","type":"book-chapter","created":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T07:15:17Z","timestamp":1756624517000},"page":"134-147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Semi-supervised Learning with\u00a0a\u00a0Meta-feature Based Safeguard System"],"prefix":"10.1007","author":[{"given":"Martin","family":"Schumann","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"10_CR1","unstructured":"Alcoba\u00e7a, E., Siqueira, F., Rivolli, A., Garcia, L.P.F., Oliva, J.T., de\u00a0Carvalho, A.C.P.L.F.: MFE: towards reproducible meta-feature extraction. J. Mach. Learn. Res. 21(111), 1\u20135 (2020)"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: Proc IJCNN, pp.\u00a01\u20138 (2020)","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"10_CR3","unstructured":"Autogluon authors: predictor.py. https:\/\/github.com\/autogluon\/autogluon\/blob\/0e3bc0e54ab4b0edf865c8b99e1418472006d6b7\/tabular\/src\/autogluon\/tabular\/predictor\/predictor.py. Accessed 26 July 2024"},{"issue":"3","key":"10_CR4","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1162\/neco.1989.1.3.295","volume":"1","author":"HB Barlow","year":"1989","unstructured":"Barlow, H.B.: Unsupervised learning. Neural Comput. 1(3), 295\u2013311 (1989)","journal-title":"Neural Comput."},{"key":"10_CR5","unstructured":"Bischl, B., et al.: OpenML benchmarking suites (2021)"},{"key":"10_CR6","unstructured":"Carlisle, M.: A Boston housing dataset controversy (2019). https:\/\/medium.com\/@docintangible\/racist-data-destruction-113e3eff54a8"},{"key":"10_CR7","unstructured":"Chouldechova, A., Roth, A.: The frontiers of fairness in machine learning. arXiv preprint: arXiv:1810.08810 (2018)"},{"key":"10_CR8","unstructured":"scikit-learn developers: Randomforestclassifier. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html"},{"key":"10_CR9","first-page":"6478","volume":"34","author":"F Ding","year":"2021","unstructured":"Ding, F., Hardt, M., Miller, J., Schmidt, L.: Retiring adult: new datasets for fair machine learning. Neurips 34, 6478\u20136490 (2021)","journal-title":"Neurips"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"van Engelen, J.E., Hoos, H.H.: Semi-supervised co-ensembling for automl. In: Trustworthy AI - Integrating Learning, Optimization and Reasoning, pp. 229\u2013250. Springer (2021)","DOI":"10.1007\/978-3-030-73959-1_21"},{"key":"10_CR11","unstructured":"Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., Smola, A.: Autogluon-tabular: robust and accurate AutoML for structured data. arXiv preprint: arXiv:2003.06505 (2020)"},{"key":"10_CR12","unstructured":"Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., Smola, A.: autogluon.tabular.TabularPredictor.fit (2024). https:\/\/auto.gluon.ai\/stable\/api\/autogluon.tabular.TabularPredictor.fit.html"},{"key":"10_CR13","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Neurips 28 (2015), pp. 2962\u20132970 (2015)"},{"key":"10_CR14","unstructured":"Fusi, N., Sheth, R., Elibol, M.: Probabilistic matrix factorization for automated machine learning. In: Neurips, vol.\u00a031 (2018)"},{"key":"10_CR15","unstructured":"Gijsbers, P., et al.: AMLB: an AutoML benchmark (2023). https:\/\/arxiv.org\/abs\/2207.12560"},{"issue":"2","key":"10_CR16","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MIS.2009.36","volume":"24","author":"A Halevy","year":"2009","unstructured":"Halevy, A., Norvig, P., Pereira, F.: The unreasonable effectiveness of data. IEEE Intell. Syst. 24(2), 8\u201312 (2009)","journal-title":"IEEE Intell. Syst."},{"key":"10_CR17","unstructured":"He, J., Gu, J., Shen, J., Ranzato, M.: Revisiting self-training for neural sequence generation. arXiv preprint: arXiv:1909.13788 (2019)"},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"89675","DOI":"10.1109\/ACCESS.2021.3090936","volume":"9","author":"M Kotlar","year":"2021","unstructured":"Kotlar, M., Punt, M., Radivojevi\u0107, Z., Cvetanovi\u0107, M., Milutinovic, V.: Novel meta-features for automated machine learning model selection in anomaly detection. IEEE Access 9, 89675\u201389687 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3090936","journal-title":"IEEE Access"},{"key":"10_CR19","unstructured":"LeCun, Y.: The MNIST database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/ (1998)"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y.F., Wang, H., Wei, T., Tu, W.W.: Towards automated semi-supervised learning. In: Proceedings of the AAAI Conf.\u00a0on AI, vol.\u00a033, pp. 4237\u20134244 (2019)","DOI":"10.1609\/aaai.v33i01.33014237"},{"key":"10_CR21","unstructured":"Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proc. 49th Annual Meeting of the Association for Computational Linguistics, pp. 142\u2013150 (2011)"},{"key":"10_CR22","unstructured":"Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT press. (2012)"},{"key":"10_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR24","unstructured":"Leibnitz Rechenzentrum: Available slurm clusters and features. https:\/\/doku.lrz.de\/available-slurm-clusters-and-features-11483939.html"},{"key":"10_CR25","unstructured":"Rivolli, A., Garcia, L.P., Soares, C., Vanschoren, J., de\u00a0Carvalho, A.C.: Towards reproducible empirical research in meta-learning, pp. 32\u201352. arXiv preprint: arXiv:1808.10406 (2018)"},{"key":"10_CR26","unstructured":"Rivolli, A., Garcia, L.P.F., Soares, C., Vanschoren, J., de Carvalho, A.C.P.L.F.: Characterizing classification datasets: a study of meta-features for meta-learning. arXiv preprint: arXiv:1808.10406 (2019)"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Schwartz, R., et\u00a0al.: Towards a standard for identifying and managing bias in artificial intelligence. NIST Spec. Publ. 1270(10.6028) (2022)","DOI":"10.6028\/NIST.SP.1270"},{"issue":"3","key":"10_CR28","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1109\/TIT.1965.1053799","volume":"11","author":"H Scudder","year":"1965","unstructured":"Scudder, H.: Probability of error of some adaptive pattern-recognition machines. IEEE Trans. Inf. Theory 11(3), 363\u2013371 (1965)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Thai-Nghe, N., Gantner, Z., Schmidt-Thieme, L.: Cost-sensitive learning methods for imbalanced data. In: Proc.\u00a0IJCNN, pp.\u00a01\u20138. IEEE (2010)","DOI":"10.1109\/IJCNN.2010.5596486"},{"key":"10_CR30","unstructured":"Verma, S., Ernst, M., Just, R.: Removing biased data to improve fairness and accuracy. arXiv preprint: arXiv:2102.03054 (2021)"},{"key":"10_CR31","unstructured":"Yao, Q., et al.: Taking human out of learning applications: a survey on automated machine learning. arXiv preprint: arXiv:1810.13306 (2018)"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proc.\u00a0of KDD-2013, pp. 847\u2013855 (2013)","DOI":"10.1145\/2487575.2487629"},{"key":"10_CR33","doi-asserted-by":"publisher","unstructured":"Mohr, F., Wever, M., H\u00fcllermeier, E.: ML-Plan: automated machine learning via hierarchical planning. Mach. Learn. 107(8\u201310), 1495\u20131515 (2018). https:\/\/doi.org\/10.1007\/s10994-018-5735-z","DOI":"10.1007\/s10994-018-5735-z"},{"key":"10_CR34","unstructured":"Lee, D.-H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, Atlanta, vol. 3, no. 2, p. 896 (2013)"}],"container-title":["Lecture Notes in Computer Science","KI 2025: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02813-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T06:01:58Z","timestamp":1757484118000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02813-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"ISBN":["9783032028129","9783032028136"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02813-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"1 September 2025","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":"Potsdam","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"48","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ki2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ki2025.gi.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}