{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:21:54Z","timestamp":1759191714186,"version":"3.44.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032059802","type":"print"},{"value":"9783032059819","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"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\/978-3-032-05981-9_8","type":"book-chapter","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T19:03:49Z","timestamp":1759172629000},"page":"119-135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["How to\u00a0RETIRE Tabular Data in\u00a0Favor of\u00a0Discrete Digital Signal Representation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4224-6709","authenticated-orcid":false,"given":"Pawe\u0142","family":"Zyblewski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8437-5592","authenticated-orcid":false,"given":"Szymon","family":"Wojciechowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"issue":"8","key":"8_CR1","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1162\/089976699300016007","volume":"11","author":"E Alpaydm","year":"1999","unstructured":"Alpaydm, E.: Combined 5 $$\\times $$ 2 CV F test for comparing supervised classification learning algorithms. Neural Comput. 11(8), 1885\u20131892 (1999)","journal-title":"Neural Comput."},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Arik, S.\u00d6., Pfister, T.: TabNet: attentive interpretable tabular learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 6679\u20136687 (2021)","DOI":"10.1609\/aaai.v35i8.16826"},{"key":"8_CR3","unstructured":"Borisov, V., Leemann, T., Se\u00dfler, K., Haug, J., Pawelczyk, M., Kasneci, G.: Deep neural networks and tabular data: a survey. IEEE Trans. Neural Netw. Learn. Syst. (2022)"},{"issue":"2","key":"8_CR4","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2015","unstructured":"Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutorials 18(2), 1153\u20131176 (2015)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Damri, A., Last, M., Cohen, N.: Towards efficient image-based representation of tabular data. Neural Comput. Appl., 1\u201321 (2023)","DOI":"10.1007\/s00521-023-09074-y"},{"key":"8_CR7","first-page":"255","volume":"17","author":"J Derrac","year":"2015","unstructured":"Derrac, J., Garcia, S., Sanchez, L., Herrera, F.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult. Valued Logic Soft Comput. 17, 255\u2013287 (2015)","journal-title":"J. Mult. Valued Logic Soft Comput."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Gimenez, M., Palanca, J., Botti, V.: Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis. Neurocomputing 378, 315\u2013323 (2020)","DOI":"10.1016\/j.neucom.2019.08.096"},{"issue":"1","key":"8_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00305-w","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock, J.T., Khoshgoftaar, T.M.: Survey on categorical data for neural networks. J. Big Data 7(1), 1\u201341 (2020). https:\/\/doi.org\/10.1186\/s40537-020-00305-w","journal-title":"J. Big Data"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR11","unstructured":"Huang, L.K., Huang, J., Rong, Y., Yang, Q., Wei, Y.: Frustratingly easy transferability estimation. In: International Conference on Machine Learning, pp. 9201\u20139225. PMLR (2022)"},{"key":"8_CR12","unstructured":"Kadra, A., Lindauer, M., Hutter, F., Grabocka, J.: Well-tuned simple nets excel on tabular datasets. In: Advances in Neural Information Processing Systems, vol. 34, pp. 23928\u201323941 (2021)"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Ke, G., Xu, Z., Zhang, J., Bian, J., Liu, T.Y.: DeepGBM: a deep learning framework distilled by GBDT for online prediction tasks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 384\u2013394 (2019)","DOI":"10.1145\/3292500.3330858"},{"issue":"10s","key":"8_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2022","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. (CSUR) 54(10s), 1\u201341 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"issue":"1","key":"8_CR16","doi-asserted-by":"publisher","first-page":"2522","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg, S.M., et al.: From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2(1), 2522\u20135839 (2020)","journal-title":"Nat. Mach. Intell."},{"key":"8_CR17","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 4765\u20134774. Curran Associates, Inc. (2017)"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhou, H., Tu, W.W., Chen, Y., Dai, W., Yang, Q.: Network on network for tabular data classification in real-world applications. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2317\u20132326 (2020)","DOI":"10.1145\/3397271.3401437"},{"key":"8_CR19","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"},{"key":"8_CR20","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":"8_CR21","doi-asserted-by":"crossref","unstructured":"Satt, A., Rozenberg, S., Hoory, R., et\u00a0al.: Efficient emotion recognition from speech using deep learning on spectrograms. In: Interspeech, pp. 1089\u20131093 (2017)","DOI":"10.21437\/Interspeech.2017-200"},{"issue":"1","key":"8_CR22","doi-asserted-by":"publisher","first-page":"11399","DOI":"10.1038\/s41598-019-47765-6","volume":"9","author":"A Sharma","year":"2019","unstructured":"Sharma, A., Vans, E., Shigemizu, D., Boroevich, K.A., Tsunoda, T.: DeepInsight: a methodology to transform a non-image data to an image for convolution neural network architecture. Sci. Rep. 9(1), 11399 (2019)","journal-title":"Sci. Rep."},{"key":"8_CR23","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","volume":"81","author":"R Shwartz-Ziv","year":"2022","unstructured":"Shwartz-Ziv, R., Armon, A.: Tabular data: deep learning is not all you need. Inf. Fusion 81, 84\u201390 (2022)","journal-title":"Inf. Fusion"},{"key":"8_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107219","volume":"104","author":"K Stapor","year":"2021","unstructured":"Stapor, K., Ksieniewicz, P., Garc\u00eda, S., Wo\u017aniak, M.: How to design the fair experimental classifier evaluation. Appl. Soft Comput. 104, 107219 (2021)","journal-title":"Appl. Soft Comput."},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Sun, B., et al.: SuperTML: two-dimensional word embedding for the precognition on structured tabular data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00360"},{"key":"8_CR26","unstructured":"Ulmer, D., Meijerink, L., Cin\u00e0, G.: Trust issues: uncertainty estimation does not enable reliable OOD detection on medical tabular data. In: Machine Learning for Health, pp. 341\u2013354. PMLR (2020)"},{"issue":"6","key":"8_CR27","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1037\/met0000374","volume":"26","author":"CJ Urban","year":"2021","unstructured":"Urban, C.J., Gates, K.M.: Deep learning: a primer for psychologists. Psychol. Methods 26(6), 743 (2021)","journal-title":"Psychol. Methods"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Z., Dai, Z., P\u00f3czos, B., Carbonell, J.: Characterizing and avoiding negative transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11293\u201311302 (2019)","DOI":"10.1109\/CVPR.2019.01155"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ding, G.: SuperTML-Clustering: two-dimensional word embedding for structured tabular data. In: International Conference on Image, Vision and Intelligent Systems, pp. 600\u2013609. Springer (2023)","DOI":"10.1007\/978-981-97-0855-0_58"},{"issue":"10","key":"8_CR30","doi-asserted-by":"publisher","first-page":"5125","DOI":"10.1109\/TNNLS.2021.3069058","volume":"33","author":"Q Zhang","year":"2021","unstructured":"Zhang, Q., Cao, L., Shi, C., Niu, Z.: Neural time-aware sequential recommendation by jointly modeling preference dynamics and explicit feature couplings. IEEE Trans. Neural Netw. Learn. Syst. 33(10), 5125\u20135137 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"8_CR31","doi-asserted-by":"publisher","first-page":"11325","DOI":"10.1038\/s41598-021-90923-y","volume":"11","author":"Y Zhu","year":"2021","unstructured":"Zhu, Y., et al.: Converting tabular data into images for deep learning with convolutional neural networks. Sci. Rep. 11(1), 11325 (2021)","journal-title":"Sci. Rep."},{"issue":"1","key":"8_CR32","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43\u201376 (2020)","journal-title":"Proc. IEEE"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Zyblewski, P.: Employing two-dimensional word embedding for difficult tabular data stream classification. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 73\u201389. Springer (2024)","DOI":"10.1007\/978-3-031-70371-3_5"}],"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\/978-3-032-05981-9_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T19:04:03Z","timestamp":1759172643000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05981-9_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"ISBN":["9783032059802","9783032059819"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05981-9_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"23 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"15 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":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}