{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:31:33Z","timestamp":1742952693144,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030623647"},{"type":"electronic","value":"9783030623654"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62365-4_7","type":"book-chapter","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T06:02:51Z","timestamp":1603951371000},"page":"66-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Stabilization of Dataset Matrix Form for Classification Dataset Generation and Algorithm Selection"],"prefix":"10.1007","author":[{"given":"Ilya","family":"Sahipov","sequence":"first","affiliation":[]},{"given":"Alexey","family":"Zabashta","sequence":"additional","affiliation":[]},{"given":"Andrey","family":"Filchenkov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"issue":"9","key":"7_CR1","doi-asserted-by":"publisher","first-page":"735","DOI":"10.3844\/jcssp.2006.735.739","volume":"2","author":"L Al Shalabi","year":"2006","unstructured":"Al Shalabi, L., Shaaban, Z., Kasasbeh, B.: Data mining: a preprocessing engine. J. Comput. Sci. 2(9), 735\u2013739 (2006)","journal-title":"J. Comput. Sci."},{"issue":"04","key":"7_CR2","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1142\/S0218213001000647","volume":"10","author":"K Alexandros","year":"2001","unstructured":"Alexandros, K., Melanie, H.: Model selection via meta-learning: a comparative study. Int. J. Artif. Intell. Tools 10(04), 525\u2013554 (2001)","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"7_CR3","volume-title":"Metalearning: Applications to Data Mining","author":"P Brazdil","year":"2008","unstructured":"Brazdil, P., Carrier, C.G., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer Science & Business Media, New York (2008)"},{"issue":"5","key":"7_CR4","first-page":"19","volume":"8","author":"E Damghanijazi","year":"2017","unstructured":"Damghanijazi, E., Mazidi, A.: Meta-heuristic approaches for solving travelling salesman problem. Int. J. Adv. Res. Comput. Sci. 8(5), 19 (2017)","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"7_CR5","unstructured":"Giraud-Carrier, C.: Metalearning-a tutorial. In: Tutorial at the 7th International Conference on Machine Learning and Applications (ICMLA), San Diego, California, USA (2008)"},{"key":"7_CR6","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Kachalsky, I., Zabashta, A., Filchenkov, A., Korneev, G.: Generating datasets for classification task and predicting best classifiers with conditional generative adversarial networks. In: Proceedings of the 2019 3rd International Conference on Advances in Artificial Intelligence, pp. 97\u2013101 (2019)","DOI":"10.1145\/3369114.3369153"},{"issue":"8","key":"7_CR8","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","unstructured":"Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455\u20135516 (2020). https:\/\/doi.org\/10.1007\/s10462-020-09825-6","journal-title":"Artif. Intell. Rev."},{"issue":"10","key":"7_CR9","first-page":"1995","volume":"3361","author":"Y LeCun","year":"1995","unstructured":"LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. Handb. Brain Theor. Neural Networks 3361(10), 1995 (1995)","journal-title":"Handb. Brain Theor. Neural Networks"},{"key":"7_CR10","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014). arXiv preprint arXiv:1411.1784"},{"key":"7_CR11","volume-title":"Bridge to Abstract Mathematics: Mathematical Proof and Structures","author":"RP Morash","year":"1991","unstructured":"Morash, R.P.: Bridge to Abstract Mathematics: Mathematical Proof and Structures. McGraw-Hill College, New York (1991)"},{"key":"7_CR12","first-page":"00085","volume":"38","author":"C Nilsson","year":"2003","unstructured":"Nilsson, C.: Heuristics for the traveling salesman problem. Linkoping Univ. 38, 00085\u20139 (2003)","journal-title":"Linkoping Univ."},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., Kim, Y.: Data synthesis based on generative adversarial networks. Proceedings of the VLDB Endowment, vol. 11, p. 10 (2018)","DOI":"10.14778\/3231751.3231757"},{"key":"7_CR14","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8026\u20138037 (2019)"},{"key":"7_CR15","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":"7_CR16","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/S0065-2458(08)60520-3","volume":"15","author":"JR Rice","year":"1976","unstructured":"Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65\u2013118 (1976). Elsevier","journal-title":"Adv. Comput."},{"key":"7_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1007\/978-3-030-29859-3_32","volume-title":"Hybrid Artificial Intelligent Systems","author":"AJ Tall\u00f3n-Ballesteros","year":"2019","unstructured":"Tall\u00f3n-Ballesteros, A.J., Fong, S., Leal-D\u00edaz, R.: Does the order of attributes play an important role in classification? In: P\u00e9rez Garc\u00eda, H., S\u00e1nchez Gonz\u00e1lez, L., Castej\u00f3n Limas, M., Quinti\u00e1n Pardo, H., Corchado Rodr\u00edguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 370\u2013380. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29859-3_32"},{"issue":"6","key":"7_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136624","volume":"50","author":"V Uurtio","year":"2017","unstructured":"Uurtio, V., Monteiro, J.M., Kandola, J., Shawe-Taylor, J., Fernandez-Reyes, D., Rousu, J.: A tutorial on canonical correlation methods. ACM Comput. Surv. (CSUR) 50(6), 1\u201333 (2017)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"2","key":"7_CR19","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2013","unstructured":"Vanschoren, J., van Rijn, J.N., Bischl, B., Torgo, L.: Openml: networked science in machine learning. SIGKDD Explor. 15(2), 49\u201360 (2013)","journal-title":"SIGKDD Explor."},{"key":"7_CR20","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. In: Advances in Neural Information Processing Systems, pp. 7335\u20137345 (2019)"},{"key":"7_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1007\/978-3-030-33607-3_43","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2019","author":"A Zabashta","year":"2019","unstructured":"Zabashta, A., Filchenkov, A.: Active dataset generation for meta-learning system quality improvement. In: Yin, H., Camacho, D., Tino, P., Tall\u00f3n-Ballesteros, A.J., Menezes, R., Allmendinger, R. (eds.) IDEAL 2019. LNCS, vol. 11871, pp. 394\u2013401. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33607-3_43"}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62365-4_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:04:05Z","timestamp":1710266645000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-62365-4_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030623647","9783030623654"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62365-4_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"27 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guimaraes","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/islab.di.uminho.pt\/ideal2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"93","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"69% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}