{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:04:28Z","timestamp":1742911468806,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031784675"},{"type":"electronic","value":"9783031784682"}],"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\/978-3-031-78468-2_12","type":"book-chapter","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:07:41Z","timestamp":1734480461000},"page":"150-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Effectiveness of Automatic Data Transformation in Deep Learning Model for Leasing Decision Support Process"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0447-4038","authenticated-orcid":false,"given":"Agata","family":"Kozina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7870-7485","authenticated-orcid":false,"given":"Micha\u0142","family":"Nadolny","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3832-8154","authenticated-orcid":false,"given":"Marcin","family":"Hernes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"12_CR1","unstructured":"Hodnett, M., Wiley, J.F.: R deep learning essentials: a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet, Second edition. Birmingham: Packt Publishing Ltd, (2018)"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"He, Y.: Transform-data-by-example (TDE): extensible data transformation in excel. In: Proceedings of the 2018 International Conference on Management of Data, s. 1785\u20131788. Houston TX USA: ACM (maj 2018). https:\/\/doi.org\/10.1145\/3183713.3193539","DOI":"10.1145\/3183713.3193539"},{"key":"12_CR3","doi-asserted-by":"publisher","unstructured":"Jin, Z., He, Y., Chauduri, S.: Auto-transform: learning-to-transform by patterns. Proc. VLDB Endow., t. 13, nr 12, s. 2368\u20132381, sie. (2020). https:\/\/doi.org\/10.14778\/3407790.3407831","DOI":"10.14778\/3407790.3407831"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Icke, I., Rosenberg, A.: Automated measures for interpretable dimensionality reduction for visual classification: a user study. In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), s. 281\u2013282. Providence, RI, USA: IEEE (pa\u017a. 2011). https:\/\/doi.org\/10.1109\/VAST.2011.6102474","DOI":"10.1109\/VAST.2011.6102474"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Mayo, M., Daoud, M.: Data normalisation using differential evolution and aggregated logistic functions. In: 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, s. 920\u2013927. New Zealand: IEEE (cze. 2019). https:\/\/doi.org\/10.1109\/CEC.2019.8790251","DOI":"10.1109\/CEC.2019.8790251"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M.H., Robardet, C., Red.: Machine learning and knowledge discovery in databases: European Conference, ECML PKDD 2019, W\u00fcrzburg, Germany, September 16\u201320, 2019: proceedings. Part 3. w Lecture notes in computer science, no. 11908. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-46150-8"},{"key":"12_CR7","unstructured":"Gandhi, A.: Data augmentation | How to use deep learning when you have limited data\u2014Part 2. (2021) [Online]. Dost\u0119pne na: https:\/\/nanonets.com\/blog\/data-augmentation-how-to-use-deep-learning-when-you-have-limited-data-part-2\/"},{"key":"12_CR8","unstructured":"Coulombe, P.G., Leroux, M., Stevanovic, D., Surprenant, S.: Macroeconomic data transformations matter. nr arXiv:2008.01714. arXiv, 9 marzec 2021. Dost\u0119p: 26 grudzie\u0144 2022. [Online]. Dost\u0119pne na: http:\/\/arxiv.org\/abs\/2008.01714"},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Koppl, R.: Strategic choice in linear sequential unmasking. Sci. Justice, t. 59, nr 2, Art. nr 2, (mar. 2019). https:\/\/doi.org\/10.1016\/j.scijus.2018.10.010","DOI":"10.1016\/j.scijus.2018.10.010"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Jazinaninejad, M., Seyedhosseini, S.M., Hosseini-Motlagh, S.-M., Nematollahi, M.: Coordinated decision-making on manufacturer\u2019s EPQ-based and buyer\u2019s period review inventory policies with stochastic price-sensitive demand: a credit option approach. RAIRO - Oper. Res., t. 53, nr 4, Art. nr 4, pa\u017a. 2019, https:\/\/doi.org\/10.1051\/ro\/2018038","DOI":"10.1051\/ro\/2018038"},{"key":"12_CR11","doi-asserted-by":"publisher","unstructured":"Hassouna, M., Tarhini, A., Elyas, T., Abou Trab, M.S.: Customer churn in mobile markets: a comparison of techniques. Int. Bus. Res., t. 8, nr 6, Art. nr 6 (maj 2015), https:\/\/doi.org\/10.5539\/ibr.v8n6p224","DOI":"10.5539\/ibr.v8n6p224"},{"key":"12_CR12","unstructured":"Brownlee, J.: What is data preparation in a machine learning project. Mach. Learn. Mastery, (2020) [Online]. Dost\u0119pne na: https:\/\/machinelearningmastery.com\/what-is-data-preparation-in-machine-learning\/"},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Hernes, M.: Deep learning for repayment prediction in leasing companies. Eur. Res. Stud. J., t. XXIV, nr Issue 2, Art. nr Issue 2 (maj 2021), https:\/\/doi.org\/10.35808\/ersj\/2178","DOI":"10.35808\/ersj\/2178"},{"key":"12_CR14","unstructured":"Biswal, A.: Top 10 deep learning algorithms you should know in 2023\u201d, Simpli learn. Dost\u0119p: 7 sierpie\u0144 2021. [Online]. Dost\u0119pne na: https:\/\/www.simplilearn.com\/tutorials\/deep-learning-tutorial\/deep-learning-algorithm"},{"key":"12_CR15","unstructured":"Roger Grosse: Lecture 9: Generalization (2018). Dost\u0119p: 8 czerwiec 2023. [Online]. Dost\u0119pne na: https:\/\/www.cs.toronto.edu\/~lczhang\/321\/notes\/notes09.pdf"},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Batyrshin, I.: Towards a general theory of similarity and association measures: Similarity, dissimilarity and correlation functions. J. Intell. Fuzzy Syst., t. 36, nr 4, s. 2977\u20133004 (kwi. 2019). https:\/\/doi.org\/10.3233\/JIFS-181503","DOI":"10.3233\/JIFS-181503"},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"Nascimben, M., Venturin, M., Rimondini, L.: Double-stage discretization approaches for biomarker-based bladder cancer survival modelling. Commun. Appl. Ind. Math., t. 12, nr 1, s. 29\u201347 (sty. 2021), https:\/\/doi.org\/10.2478\/caim-2021-0003","DOI":"10.2478\/caim-2021-0003"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Omozaki, Y., Masuyama, N., Nojima, Y., Ishibuchi, H.: Multiobjective fuzzy genetics-based machine learning for multi-label classification. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), s. 1\u20138. Glasgow, United Kingdom: IEEE (lip. 2020). https:\/\/doi.org\/10.1109\/FUZZ48607.2020.9177804","DOI":"10.1109\/FUZZ48607.2020.9177804"},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Yan, S., Bouaziz, S., Lee, D., Barlow, J.: Semi-supervised dimensionality reduction for analyzing high-dimensional data with constraints. Neurocomputing, t. 76, nr 1, s. 114\u2013124, (sty. 2012). https:\/\/doi.org\/10.1016\/j.neucom.2011.03.057","DOI":"10.1016\/j.neucom.2011.03.057"},{"key":"12_CR20","doi-asserted-by":"publisher","unstructured":"Kolisetty, V., rajput, D.: A review on the significance of machine learning for data analysis in big data. Jordanian J. Comput. Inf. Technol., nr 0, s. 1 (2019), https:\/\/doi.org\/10.5455\/jjcit.71-1564729835","DOI":"10.5455\/jjcit.71-1564729835"},{"key":"12_CR21","unstructured":"Hofmann, M., Klinkenberg, R., Red.: RapidMiner: data mining use cases and business analytics applications. w Chapman & Hall\/CRC data mining and knowledge discovery series, no. 33. CRC Press, Boca Raton (2014)"},{"key":"12_CR22","unstructured":"Fowler, M., Parsons, R.: Domain-specific languages. In: The Addison-Wesley signature series. Upper Saddle River, NJ Boston Indianapolis San Francisco New York Toronto Montreal London Munich Paris Madrid Sydney Tokyo Singapore Mexico City: Addison-Wesley (2011)"},{"key":"12_CR23","unstructured":"Contreras-Ochand, L., Ferri, C., Hern\u00e1ndez-Orallo, J., Mart\u00ednez-Plumed, F., Ram\u00edrez-Quintana, M.J., Katayama, S.: Automated data transformation with inductive programming and dynamic background knowledge. Mach. Learn. Knowl. Discov. Databases, s. 735\u2013751 (2019)"},{"key":"12_CR24","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: AutoAugment: learning augmentation policies from data. arXiv, 11 kwiecie\u0144 2019. Dost\u0119p: 3 listopad 2023. [Online]. Dost\u0119pne na: http:\/\/arxiv.org\/abs\/1805.09501"},{"key":"12_CR25","doi-asserted-by":"publisher","unstructured":"Saxena, A., Bhagat, V.V., Robins, B.: Insurance data analysis with COGNITO: an auto analysing and storytelling Python library. In: 2021 International Conference on Intelligent Technologies (CONIT), s. 1\u20136, Hubli, India: IEEE (cze. 2021). https:\/\/doi.org\/10.1109\/CONIT51480.2021.9498523","DOI":"10.1109\/CONIT51480.2021.9498523"},{"key":"12_CR26","unstructured":"Georgian: Choosing the best AutoML framework. Georgian Impact Blog. Dost\u0119p: (20 maj 2019). [Online]. Dost\u0119pne na: https:\/\/medium.com\/georgian-impact-blog\/choosing-the-best-automl-framework-4f2a90cb1826"},{"key":"12_CR27","doi-asserted-by":"publisher","unstructured":"Ribeiro, R., Pilastri, A., Moura, C., Rodrigues, F., Rocha, R., Cortez, P.: Predicting the tear strength of woven fabrics via automated machine learning: an application of the CRISP-DM methodology. In Proceedings of the 22nd International Conference on Enterprise Information Systems, Prague, s. 548\u2013555, Czech Republic: SCITEPRESS - Science and Technology Publications (2020). https:\/\/doi.org\/10.5220\/0009411205480555","DOI":"10.5220\/0009411205480555"},{"key":"12_CR28","doi-asserted-by":"publisher","unstructured":"Contreras-Ochando, L., Ferri, C., Hern\u00e1ndez-Orallo, J., Mart\u00ednez-Plumed, F., Ram\u00edrez-Quintana, M.J., Katayama, S.: BK-ADAPT: dynamic background knowledge for automating data transformation. Mach. Learn. Knowl. Discov. Databases, s. 755\u2013759 (2019), https:\/\/doi.org\/10.1007\/978-3-030-46133-1_45","DOI":"10.1007\/978-3-030-46133-1_45"},{"key":"12_CR29","unstructured":"What is hashing trick? Hashing trick explained. SoulPage (2024). Dost\u0119p: 15 kwiecie\u0144 2024. [Online]. Dost\u0119pne na: https:\/\/soulpageit.com\/ai-glossary\/hashing-trick-explained\/"},{"key":"12_CR30","unstructured":"Sztuczna inteligencja. Wydzia\u0142 Matematyki i Informatyki UWM (2012). Dost\u0119p: 12 listopad 2021. [Online]. Dost\u0119pne na: http:\/\/wmii.uwm.edu.pl\/~ksopyla\/wp-content\/uploads\/2012\/03\/cw2_miary-oceny-klasyfikacji.pdf"},{"key":"12_CR31","doi-asserted-by":"publisher","unstructured":"Sharma, R., Patel, M.: Toxic comment classification using neural networks and machine learning. IARJSET, t. 5, nr 9, s. 47\u201352 (wrz. 2018), https:\/\/doi.org\/10.17148\/IARJSET.2018.597","DOI":"10.17148\/IARJSET.2018.597"},{"key":"12_CR32","unstructured":"Dokumentacja TensorFlow. Python. Dost\u0119p: 1 czerwiec 2023. [Online]. Dost\u0119pne na: https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/preprocessing\/text\/hashing_trick"},{"key":"12_CR33","unstructured":"Kant, U.: The complete guide to encoding categorical features. Utkarsh Kant KantsChants.com. Dost\u0119p: 15 kwiecie\u0144 2024. [Online]. Dost\u0119pne na: https:\/\/kantschants.com\/complete-guide-to-encoding-categorical-features"},{"key":"12_CR34","doi-asserted-by":"publisher","unstructured":"Tapinos, A., Constantinides, B., Phan, M.V.T., Kouchaki, S., Cotten, M., Robertson, D.L.: The utility of data transformation for alignment, De Novo assembly and classification of short read virus sequences. Viruses, t. 11, nr 5, Art. nr 5 (kwi. 2019), https:\/\/doi.org\/10.3390\/v11050394","DOI":"10.3390\/v11050394"},{"key":"12_CR35","doi-asserted-by":"publisher","unstructured":"Shyu, M.-L., Sarinnapakorn, K., Kuruppu-Appuhamilage, I., Chen, S.-C., Chang, L.W., Goldring, T.: Handling nominal features in anomaly intrusion detection problems. In: 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA\u201905), s. 55\u201362. Tokyo, IEEE (2005). https:\/\/doi.org\/10.1109\/RIDE.2005.10","DOI":"10.1109\/RIDE.2005.10"}],"container-title":["Lecture Notes in Networks and Systems","Emerging Challenges in Intelligent Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78468-2_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T01:03:28Z","timestamp":1734483808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78468-2_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031784675","9783031784682"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78468-2_12","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santiago de Compostela","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}