{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:52:44Z","timestamp":1743011564083,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031627989"},{"type":"electronic","value":"9783031627996"}],"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\/978-3-031-62799-6_9","type":"book-chapter","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T11:27:56Z","timestamp":1718105276000},"page":"81-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Objective Lagged Feature Selection Based on\u00a0Dependence Coefficient for\u00a0Time-Series Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6975-1932","authenticated-orcid":false,"given":"Mar\u00eda Lourdes","family":"Linares-Barrera","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8514-4182","authenticated-orcid":false,"given":"Manuel J. Jim\u00e9nez","family":"Navarro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8243-2186","authenticated-orcid":false,"given":"Jos\u00e9 C.","family":"Riquelme","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3160-7414","authenticated-orcid":false,"given":"Mar\u00eda","family":"Mart\u00ednez-Ballesteros","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"issue":"6","key":"9_CR1","doi-asserted-by":"publisher","first-page":"3070","DOI":"10.1214\/21-AOS2073","volume":"49","author":"M Azadkia","year":"2021","unstructured":"Azadkia, M., Chatterjee, S.: A simple measure of conditional dependence. Ann. Stat. 49(6), 3070\u20133102 (2021)","journal-title":"Ann. Stat."},{"key":"9_CR2","first-page":"2653","volume":"18","author":"A Benavoli","year":"2017","unstructured":"Benavoli, A., Corani, G., Demsar, J., Zaffalon, M.: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. J. Mach. Learn. Res. 18, 2653\u20132688 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Bol\u00f3n-Canedo, V., Alonso-Betanzos, A., Mor\u00e1n-Fern\u00e1ndez, L., Cancela, B.: Feature selection: from the past to the future. In: Advances in Selected Artificial Intelligence Areas: World Outstanding Women in Artificial Intelligence, pp. 11\u201334 (2022)","DOI":"10.1007\/978-3-030-93052-3_2"},{"key":"9_CR4","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","volume":"300","author":"J Cai","year":"2018","unstructured":"Cai, J., Luo, J., Wang, S., Yang, S.: Feature selection in machine learning: a new perspective. Neurocomputing 300, 70\u201379 (2018)","journal-title":"Neurocomputing"},{"key":"9_CR5","unstructured":"CDT: California department of transportation (2015)"},{"issue":"2","key":"9_CR6","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"9_CR7","unstructured":"Dunn, J., Mingardi, L., Zhuo, Y.: Comparing interpretability and explainability for feature selection. arXiv preprint arXiv:2105.05328 (2021)"},{"key":"9_CR8","unstructured":"Espinosa, R., Jim\u00e9nez, F., Palma, J.: Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting. arXiv (2023)"},{"key":"9_CR9","unstructured":"Godahewa, R., Bergmeir, C., Webb, G., Hyndman, R., Montero-Manso, P.: Electricity hourly dataset (2020)"},{"key":"9_CR10","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/978-3-031-18050-7_54","volume-title":"Soft Computing Models in Industrial and Environmental Applications","author":"MJ Jim\u00e9nez-Navarro","year":"2022","unstructured":"Jim\u00e9nez-Navarro, M.J., Mart\u00ednez-Ballesteros, M., Brito, I., Mart\u00ednez-\u00c1lvarez, F., Cort\u00e9s, G.: Feature-aware drop layer (FADL): a nonparametric neural network layer for feature selection. In: Garc\u00edaBringas, P., et al. (eds.) SOCO 2022. LNCS, vol. 531, pp. 557\u2013566. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18050-7_54"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-Navarro, M., Mart\u00ednez-Ballesteros, M., Mart\u00ednez-\u00c1lvarez, F., Asencio-Cort\u00e9s, G.: Explaining deep learning models for ozone pollution prediction via embedded feature selection. Appl. Soft Comput. 111504 (2024)","DOI":"10.1016\/j.asoc.2024.111504"},{"issue":"1","key":"9_CR12","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.bbe.2022.11.005","volume":"43","author":"I Kilincer","year":"2023","unstructured":"Kilincer, I., Ertam, F., Sengur, A., Tan, R., Acharya, U.: Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization. Biocybern. Biomed. Eng. 43(1), 30\u201341 (2023)","journal-title":"Biocybern. Biomed. Eng."},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W., Yang, Y., Liu, H.: Modeling long- and short-term temporal patterns with deep neural networks. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 95\u2013104 (2018)","DOI":"10.1145\/3209978.3210006"},{"key":"9_CR14","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.neucom.2019.08.108","volume":"397","author":"X Liu","year":"2020","unstructured":"Liu, X., Zhang, H., Kong, X., Lee, K.: Wind speed forecasting using deep neural network with feature selection. Neurocomputing 397, 393\u2013403 (2020)","journal-title":"Neurocomputing"},{"key":"9_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113237","volume":"148","author":"T Niu","year":"2020","unstructured":"Niu, T., Wang, J., Lu, H., Yang, W., Du, P.: Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting. Expert Syst. Appl. 148, 113237 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"9_CR16","first-page":"26","volume":"17","author":"J Wu","year":"2019","unstructured":"Wu, J., Chen, X., Zhang, H., Xiong, L., Lei, H., Deng, S.: Hyperparameter optimization for machine learning models based on Bayesian optimization. J. Electron. Sci. Technol. 17(1), 26\u201340 (2019)","journal-title":"J. Electron. Sci. Technol."},{"issue":"2","key":"9_CR17","doi-asserted-by":"publisher","first-page":"56","DOI":"10.38094\/jastt1224","volume":"1","author":"R Zebari","year":"2020","unstructured":"Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., Saeed, J.: A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. J. Appl. Sci. Technol. Trends 1(2), 56\u201370 (2020)","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"9_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108964","volume":"123","author":"J Zhou","year":"2022","unstructured":"Zhou, J., Hua, Z.: A correlation guided genetic algorithm and its application to feature selection. Appl. Soft Comput. 123, 108964 (2022)","journal-title":"Appl. Soft Comput."}],"container-title":["Lecture Notes in Computer Science","Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62799-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T11:31:47Z","timestamp":1718105507000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62799-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031627989","9783031627996"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_9","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":"12 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAEPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference of the Spanish Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"A Coru\u00f1a","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":"19 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 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":"caepia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/caepia24.aepia.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}