{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:58:05Z","timestamp":1742932685377,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031414558"},{"type":"electronic","value":"9783031414565"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-41456-5_59","type":"book-chapter","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T23:02:43Z","timestamp":1694559763000},"page":"782-793","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Goal-Oriented Classification of\u00a0Football Results"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1137-4350","authenticated-orcid":false,"given":"Szymon","family":"G\u0142owania","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2128-6998","authenticated-orcid":false,"given":"Jan","family":"Kozak","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7893-5410","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Juszczuk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"59_CR1","doi-asserted-by":"crossref","unstructured":"Ahmad, M.A., Eckert, C., Teredesai, A.: Interpretable machine learning in healthcare. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 559\u2013560 (2018)","DOI":"10.1145\/3233547.3233667"},{"key":"59_CR2","first-page":"159","volume":"1","author":"SM Arabzad","year":"2014","unstructured":"Arabzad, S.M., Tayebi Araghi, M.E., Sadi-Nezhad, S., Ghofrani, N.: Football match results prediction using artificial neural networks: the case of Iran pro league. J. Appl. Res. Ind. Eng. 1, 159\u2013179 (2014)","journal-title":"J. Appl. Res. Ind. Eng."},{"issue":"2","key":"59_CR3","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1016\/j.ijforecast.2018.01.003","volume":"35","author":"R Babota","year":"2019","unstructured":"Babota, R., Kaur, H.: Predictive analysis and modelling football results using machine learning approach for English premier league. Int. J. Forecast. 35(2), 741\u2013755 (2019)","journal-title":"Int. J. Forecast."},{"issue":"2","key":"59_CR4","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/S0169-2070(01)00144-3","volume":"19","author":"BL Boulier","year":"2003","unstructured":"Boulier, B.L., Stekler, H.O.: Neural network prediction of NFL football games. Int. J. Forecast. 19(2), 257\u2013270 (2003)","journal-title":"Int. J. Forecast."},{"key":"59_CR5","unstructured":"Breiman, L., Friedman, J., Stone, C., Olshen, R.: Classification and regression trees. Chapman & Hall, New York (1984)"},{"issue":"2","key":"59_CR6","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996). https:\/\/doi.org\/10.1007\/BF00058655","journal-title":"Mach. Learn."},{"issue":"1","key":"59_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"issue":"1","key":"59_CR8","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.aci.2017.09.005","volume":"15","author":"RP Bunker","year":"2019","unstructured":"Bunker, R.P., Thabtah, F.: A machine learning framework for sport result prediction. Appl. Comput. Inform. 15(1), 27\u201333 (2019)","journal-title":"Appl. Comput. Inform."},{"key":"59_CR9","volume-title":"Advances in Financial Machine Learning","author":"ML De Prado","year":"2018","unstructured":"De Prado, M.L.: Advances in Financial Machine Learning. Wiley, Hoboken (2018)"},{"issue":"2","key":"59_CR10","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/j.ijforecast.2011.05.002","volume":"28","author":"D Delen","year":"2012","unstructured":"Delen, D., Cogdell, D., Kasap, N.: A comparative analysis of data mining methods in predicting NCAA bowl outcomes. Int. J. Forecast. 28(2), 543\u2013552 (2012). https:\/\/doi.org\/10.1016\/j.ijforecast.2011.05.002","journal-title":"Int. J. Forecast."},{"key":"59_CR11","unstructured":"Dorigo, M.: Optimization, learning and natural algorithms (in Italian). Ph.D. thesis, vol. 192, pp. 1573\u20131582 (1992)"},{"key":"59_CR12","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871\u20131874 (2008). https:\/\/doi.org\/10.1023\/B:STCO.0000035301.49549.88","journal-title":"J. Mach. Learn. Res."},{"key":"59_CR13","unstructured":"Fernandez, M., Ulmer, B.: Predicting soccer match results in the English premier league (2014)"},{"issue":"1","key":"59_CR14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119\u2013139 (1997). https:\/\/doi.org\/10.1006\/jcss.1997.1504","journal-title":"J. Comput. Syst. Sci."},{"key":"59_CR15","unstructured":"Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148\u2013156. Citeseer (1996)"},{"key":"59_CR16","doi-asserted-by":"publisher","first-page":"3393","DOI":"10.1016\/j.procs.2022.09.398","volume":"207","author":"S G\u0142owania","year":"2022","unstructured":"G\u0142owania, S., Kozak, J., Juszczuk, P.: New voting schemas for heterogeneous ensemble of classifiers in the problem of football results prediction. Procedia Comput. Sci. 207, 3393\u20133402 (2022)","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"59_CR17","first-page":"11","volume":"23","author":"A Hasan","year":"2018","unstructured":"Hasan, A., Moin, S., Karim, A., Shamshirband, S.: Machine learning-based sentiment analysis for twitter accounts. Math. Comput. Appl. 23(1), 11 (2018)","journal-title":"Math. Comput. Appl."},{"key":"59_CR18","doi-asserted-by":"publisher","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 2. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-21606-5","DOI":"10.1007\/978-0-387-21606-5"},{"issue":"7","key":"59_CR19","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.knosys.2006.04.011","volume":"19","author":"A Joseph","year":"2006","unstructured":"Joseph, A., Fenton, N.E., Neil, M.: Predicting football results using Bayesian nets and other machine learning techniques. Knowl.-Based Syst. 19(7), 544\u2013553 (2006)","journal-title":"Knowl.-Based Syst."},{"issue":"12","key":"59_CR20","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.3390\/e23121621","volume":"23","author":"P Juszczuk","year":"2021","unstructured":"Juszczuk, P., Kozak, J., Dziczkowski, G., G\u0142owania, S., Jach, T., Probierz, B.: Real-world data difficulty estimation with the use of entropy. Entropy 23(12), 1621 (2021). https:\/\/doi.org\/10.3390\/e23121621","journal-title":"Entropy"},{"key":"59_CR21","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93752-6","volume-title":"Decision Tree and Ensemble Learning Based on Ant Colony Optimization","author":"J Kozak","year":"2019","unstructured":"Kozak, J.: Decision Tree and Ensemble Learning Based on Ant Colony Optimization. SCI, vol. 781. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-93752-6"},{"key":"59_CR22","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.knosys.2014.11.027","volume":"75","author":"J Kozak","year":"2015","unstructured":"Kozak, J., Boryczka, U.: Multiple boosting in the ant colony decision forest meta-classifier. Knowl.-Based Syst. 75, 141\u2013151 (2015)","journal-title":"Knowl.-Based Syst."},{"key":"59_CR23","doi-asserted-by":"publisher","first-page":"1573","DOI":"10.1016\/j.procs.2021.08.161","volume":"192","author":"J Kozak","year":"2021","unstructured":"Kozak, J., G\u0142owania, S.: Heterogeneous ensembles of classifiers in predicting Bundesliga football results. Procedia Comput. Sci. 192, 1573\u20131582 (2021). https:\/\/doi.org\/10.1016\/j.procs.2021.08.161","journal-title":"Procedia Comput. Sci."},{"key":"59_CR24","unstructured":"Kozak, J., G\u0142owania, S.: Bundesliga football results (2021). https:\/\/www.ue.katowice.pl\/index.php?id=20435"},{"key":"59_CR25","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.sbspro.2014.02.249","volume":"117","author":"A Maszczyk","year":"2014","unstructured":"Maszczyk, A., Go\u0142a\u015b, A., Pietraszewski, P., Roczniok, R., Zaj\u0105c, A., Stanula, A.: Application of neural and regression models in sports results prediction. Procedia Soc. Behav. Sci. 117, 482\u2013487 (2014). https:\/\/doi.org\/10.1016\/j.sbspro.2014.02.249","journal-title":"Procedia Soc. Behav. Sci."},{"key":"59_CR26","doi-asserted-by":"publisher","unstructured":"McCabe, A., Trevathan, J.: Artificial intelligence in sports prediction. In: Fifth International Conference on Information Technology: New Generations (ITNG 2008), pp. 1194\u20131197. IEEE (2008). https:\/\/doi.org\/10.1109\/ITNG.2008.203","DOI":"10.1109\/ITNG.2008.203"},{"issue":"7","key":"59_CR27","doi-asserted-by":"publisher","first-page":"5351","DOI":"10.1016\/j.aej.2021.08.084","volume":"61","author":"Y Men","year":"2022","unstructured":"Men, Y.: Intelligent sports prediction analysis system based on improved gaussian fuzzy algorithm. Alex. Eng. J. 61(7), 5351\u20135359 (2022)","journal-title":"Alex. Eng. J."},{"issue":"2","key":"59_CR28","first-page":"217","volume":"6","author":"NH Nguyen","year":"2022","unstructured":"Nguyen, N.H., Nguyen, D.T.A., Ma, B., Hu, J.: The application of machine learning and deep learning in sport: predicting NBA players\u2019 performance and popularity. J. Inf. Telecommun. 6(2), 217\u2013235 (2022)","journal-title":"J. Inf. Telecommun."},{"issue":"12","key":"59_CR29","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.1007\/s00521-016-2321-9","volume":"28","author":"PF Pai","year":"2017","unstructured":"Pai, P.F., ChangLiao, L.H., Lin, K.P.: Analyzing basketball games by a support vector machines with decision tree model. Neural Comput. Appl. 28(12), 4159\u20134167 (2017). https:\/\/doi.org\/10.1007\/s00521-016-2321-9","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"59_CR30","doi-asserted-by":"publisher","first-page":"113773","DOI":"10.1016\/j.sna.2022.113773","volume":"345","author":"G Paj\u0105k","year":"2022","unstructured":"Paj\u0105k, G., Krutz, P., Patalas-Maliszewska, J., Rehm, M., Paj\u0105k, I., Dix, M.: An approach to sport activities recognition based on an inertial sensor and deep learning. Sens. Actuators, A 345(1), 113773 (2022)","journal-title":"Sens. Actuators, A"},{"key":"59_CR31","doi-asserted-by":"crossref","first-page":"121461","DOI":"10.1016\/j.physa.2019.121461","volume":"528","author":"S Qiu","year":"2019","unstructured":"Qiu, S., et al.: Multi-sensor information fusion based on machine learning for real applications in human activity recognition: state-of-the-art and research challenges. Physica A: Stat. Mech. Appl. 528, 121461 (2019)","journal-title":"Physica A: Stat. Mech. Appl."},{"key":"59_CR32","doi-asserted-by":"crossref","unstructured":"Rue, H., Salvesen, O.: Prediction and retrospective analysis of soccer matches in a league. J. Royal Stat. Soc. Ser. D (2000)","DOI":"10.1111\/1467-9884.00243"},{"key":"59_CR33","unstructured":"Schauberger, G., Groll, A., Tutz, G.: Modeling football results in the German Bundesliga using match-specific covariates. Technical report number 197 (2016)"},{"key":"59_CR34","doi-asserted-by":"publisher","first-page":"103900","DOI":"10.1016\/j.micpro.2021.103900","volume":"82","author":"H Shen","year":"2021","unstructured":"Shen, H.: Prediction simulation of sports injury based on embedded system and neural network. Microprocess. Microsyst. 82, 103900 (2021)","journal-title":"Microprocess. Microsyst."},{"issue":"4","key":"59_CR35","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.dcan.2021.08.008","volume":"8","author":"Q Zhang","year":"2022","unstructured":"Zhang, Q., Zhang, X., Hu, H., Li, C., Lin, Y., Ma, R.: Sports match prediction model for training and exercise using attention-based LSTM network. Digi. Commun. Netw. 8(4), 508\u2013515 (2022)","journal-title":"Digi. Commun. Netw."}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41456-5_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T23:09:58Z","timestamp":1694560198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41456-5_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031414558","9783031414565"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41456-5_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Budapest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hungary","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","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":"218","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":"59","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":"27% - 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":"3.01","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":"1.86","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}