{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:42:49Z","timestamp":1770741769821,"version":"3.49.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T00:00:00Z","timestamp":1621555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T00:00:00Z","timestamp":1621555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"European University Institute - Fiesole"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Auton Agent Multi-Agent Syst"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.<\/jats:p>","DOI":"10.1007\/s10458-021-09504-y","type":"journal-article","created":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T08:02:59Z","timestamp":1621584179000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Voting with random classifiers (VORACE): theoretical and experimental analysis"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5284-6487","authenticated-orcid":false,"given":"Cristina","family":"Cornelio","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9769-3899","authenticated-orcid":false,"given":"Michele","family":"Donini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9846-0157","authenticated-orcid":false,"given":"Andrea","family":"Loreggia","sequence":"additional","affiliation":[]},{"given":"Maria Silvia","family":"Pini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8898-219X","authenticated-orcid":false,"given":"Francesca","family":"Rossi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,21]]},"reference":[{"key":"9504_CR1","unstructured":"Arrow, K. J., Sen, A. K., & Suzumura, K. (2002). Handbook of social choice and welfare. North-Holland."},{"key":"9504_CR2","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1016\/j.compeleceng.2018.02.021","volume":"69","author":"T Ateeq","year":"2018","unstructured":"Ateeq, T., Majeed, M. N., Anwar, S. M., Maqsood, M., Rehman, Z., Lee, J. W., et al. (2018). Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI. Computers and Electrical Engineering, 69, 768\u2013781. https:\/\/doi.org\/10.1016\/j.compeleceng.2018.02.021.","journal-title":"Computers and Electrical Engineering"},{"key":"9504_CR3","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.jag.2018.06.009","volume":"73","author":"M Azadbakht","year":"2018","unstructured":"Azadbakht, M., Fraser, C. S., & Khoshelham, K. (2018). Synergy of sampling techniques and ensemble classifiers for classification of urban environments using full-waveform lidar data. International Journal of Applied Earth Observation and Geoinformation, 73, 277\u2013291. https:\/\/doi.org\/10.1016\/j.jag.2018.06.009.","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"issue":"3","key":"9504_CR4","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10044-003-0192-z","volume":"6","author":"R Barandela","year":"2003","unstructured":"Barandela, R., Valdovinos, R. M., & S\u00e1nchez, J. S. (2003). New applications of ensembles of classifiers. Pattern Analysis and Applications, 6(3), 245\u2013256. https:\/\/doi.org\/10.1007\/s10044-003-0192-z.","journal-title":"Pattern Analysis and Applications"},{"issue":"1\u20132","key":"9504_CR5","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1023\/A:1007515423169","volume":"36","author":"E Bauer","year":"1999","unstructured":"Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1\u20132), 105\u2013139. https:\/\/doi.org\/10.1023\/A:1007515423169.","journal-title":"Machine Learning"},{"key":"9504_CR6","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281\u2013305.","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"9504_CR7","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L. (1996a). Bagging predictors. Machine Learning, 24(2), 123\u2013140. https:\/\/doi.org\/10.1007\/BF00058655.","journal-title":"Machine Learning"},{"issue":"1","key":"9504_CR8","first-page":"49","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L. (1996b). Stacked regressions. Machine Learning, 24(1), 49\u201364.","journal-title":"Machine Learning"},{"key":"9504_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, ACM (pp. 785\u2013794).","DOI":"10.1145\/2939672.2939785"},{"key":"9504_CR10","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139923972","volume-title":"Essai sur l'application de l'analyse \u00e0 la probabilit\u00e9 des d\u00e9cisions rendues \u00e0 la pluralit\u00e9 des voix (Cambridge Library Collection - Mathematics)","author":"N. Condorcet","year":"2014","unstructured":"Condorcet, N. (2014). Essai sur l'application de l'analyse \u00e0 la probabilit\u00e9 des d\u00e9cisions rendues \u00e0 la pluralit\u00e9 des voix (Cambridge Library Collection - Mathematics). Cambridge: Cambridge University Press. https:\/\/doi.org\/10.1017\/CBO9781139923972"},{"key":"9504_CR11","unstructured":"Conitzer, V., & Sandholm, T. (2005). Common voting rules as maximum likelihood estimators. In Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (pp. 145\u2013152). Arlington, Virginia, USA: AUAI Press. http:\/\/dl.acm.org\/citation.cfm?id=3020336.3020354"},{"key":"9504_CR12","first-page":"620","volume":"6","author":"V Conitzer","year":"2006","unstructured":"Conitzer, V., Davenport, A., & Kalagnanam, J. (2006). Improved bounds for computing kemeny rankings. AAAI, 6, 620\u2013626.","journal-title":"AAAI"},{"key":"9504_CR13","unstructured":"Conitzer, V., Rognlie, M., & Xia, L. (2009). Preference functions that score rankings and maximum likelihood estimation. In IJCAI 2009, Proceedings of the 21st international joint conference on artificial intelligence, Pasadena, California, USA, July 11\u201317, 2009 (pp. 109\u2013115)."},{"key":"9504_CR14","unstructured":"Cornelio, C., Donini, M., Loreggia, A., Pini, M. S., & Rossi, F. (2020). Voting with random classifiers (VORACE). In Proceedings of the 19th international conference on autonomous agents and multi-agent systems (AAMAS) (pp. 1822\u20131824)."},{"key":"9504_CR15","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139923972","volume-title":"Essai sur l\u2019application de l\u2019analyse \u00e0 la probabilit\u00e9 des d\u00e9cisions rendues \u00e0 la pluralit\u00e9 des voix","author":"N De Condorcet","year":"2014","unstructured":"De Condorcet, N., et al. (2014). Essai sur l\u2019application de l\u2019analyse \u00e0 la probabilit\u00e9 des d\u00e9cisions rendues \u00e0 la pluralit\u00e9 des voix. Cambridge: Cambridge University Press."},{"issue":"2","key":"9504_CR16","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1023\/A:1007607513941","volume":"40","author":"TG Dietterich","year":"2000","unstructured":"Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2), 139\u2013157.","journal-title":"Machine Learning"},{"key":"9504_CR17","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1613\/jair.105","volume":"2","author":"TG Dietterich","year":"1995","unstructured":"Dietterich, T. G., & Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. The Journal of Artificial Intelligence Research, 2, 263\u2013286. https:\/\/doi.org\/10.1613\/jair.105.","journal-title":"The Journal of Artificial Intelligence Research"},{"key":"9504_CR18","unstructured":"Donini, M., Loreggia, A., Pini, M. S., & Rossi, F. (2018). Voting with random neural networks: A democratic ensemble classifier. In Proceedings of the RiCeRcA Workshop - co-located with the 17th International Conference of the Italian Association for Artificial Intelligence."},{"key":"9504_CR19","unstructured":"van Erp, M., & Schomaker, L. (2000). Variants of the borda count method for combining ranked classifier hypotheses. In 7th workshop on frontiers in handwriting recognition (pp. 443\u2013452)."},{"issue":"2017","key":"9504_CR20","first-page":"27","volume":"74","author":"P Faliszewski","year":"2017","unstructured":"Faliszewski, P., Skowron, P., Slinko, A., & Talmon, N. Multiwinner voting: A new challenge for social choice theory. Trends in Computational Social Choice, 74(2017), 27-47.","journal-title":"Trends in Computational Social Choice"},{"issue":"1","key":"9504_CR21","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. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. The Journal of Computer and System, 55(1), 119\u2013139. https:\/\/doi.org\/10.1006\/jcss.1997.1504.","journal-title":"The Journal of Computer and System"},{"key":"9504_CR22","doi-asserted-by":"crossref","unstructured":"Gandhi, I., & Pandey, M. (2015). Hybrid ensemble of classifiers using voting. In 2015 international conference on Green Computing and Internet of Things (ICGCIoT) (pp. 399\u2013404). IEEE.","DOI":"10.1109\/ICGCIoT.2015.7380496"},{"key":"9504_CR23","unstructured":"Grandi, U., Loreggia, A., Rossi, F., & Saraswat, V. (2014). From sentiment analysis to preference aggregation. In International Symposium on Artificial Intelligence and Mathematics, ISAIM."},{"issue":"3","key":"9504_CR24","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s10472-015-9488-0","volume":"77","author":"U Grandi","year":"2016","unstructured":"Grandi, U., Loreggia, A., Rossi, F., & Saraswat, V. (2016). A borda count for collective sentiment analysis. Annals of Mathematics and Artificial Intelligence, 77(3), 281\u2013302.","journal-title":"Annals of Mathematics and Artificial Intelligence"},{"key":"9504_CR25","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1007\/s11634-015-0227-5","volume":"12","author":"A Gul","year":"2018","unstructured":"Gul, A., Perperoglou, A., Khan, Z., Mahmoud, O., Miftahuddin, M., Adler, W., & Lausen, B. (2018a). Ensemble of a subset of knn classifiers. Advances Data Analysis and Classification, 12, 827\u2013840.","journal-title":"Advances Data Analysis and Classification"},{"issue":"4","key":"9504_CR26","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1007\/s11634-015-0227-5","volume":"12","author":"A Gul","year":"2018","unstructured":"Gul, A., Perperoglou, A., Khan, Z., Mahmoud, O., Miftahuddin, M., Adler, W., & Lausen, B. (2018b). Ensemble of a subset of knn classifiers. Advances Data Analysis and Classification, 12(4), 827\u2013840. https:\/\/doi.org\/10.1007\/s11634-015-0227-5.","journal-title":"Advances Data Analysis and Classification"},{"key":"9504_CR27","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1109\/ICDAR.1995.598994","volume":"1","author":"TK Ho","year":"1995","unstructured":"Ho, T. K. (1995). Random decision forests. Document analysis and recognition, IEEE, 1, 278\u2013282.","journal-title":"Document analysis and recognition, IEEE"},{"issue":"4","key":"9504_CR28","first-page":"577","volume":"88","author":"JG Kemeny","year":"1959","unstructured":"Kemeny, J. G. (1959). Mathematics without numbers. Daedalus, 88(4), 577\u2013591.","journal-title":"Daedalus"},{"issue":"3","key":"9504_CR29","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1109\/TSMCA.2010.2084081","volume":"41","author":"TM Khoshgoftaar","year":"2011","unstructured":"Khoshgoftaar, T. M., Hulse, J. V., & Napolitano, A. (2011). Comparing boosting and bagging techniques with noisy and imbalanced data. IEEE Transactions Systems, Man, and Cybernetics, Part A, 41(3), 552\u2013568. https:\/\/doi.org\/10.1109\/TSMCA.2010.2084081.","journal-title":"IEEE Transactions Systems, Man, and Cybernetics, Part A"},{"key":"9504_CR30","doi-asserted-by":"crossref","unstructured":"Kittler, J., Hatef, M., & Duin, R.P.W. (1996). Combining classifiers. In Proceedings of the Sixth International Conference on Pattern Recognition (pp. 897\u2013901). Silver Spring, MD: IEEE computer society press.","DOI":"10.1109\/ICPR.1996.547205"},{"issue":"3","key":"9504_CR31","doi-asserted-by":"publisher","first-page":"239","DOI":"10.3233\/KES-2005-9308","volume":"9","author":"SB Kotsiantis","year":"2005","unstructured":"Kotsiantis, S. B., & Pintelas, P. E. (2005). Local voting of weak classifiers. KES Journal, 9(3), 239\u2013248.","journal-title":"KES Journal"},{"issue":"3","key":"9504_CR32","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s10462-007-9052-3","volume":"26","author":"SB Kotsiantis","year":"2006","unstructured":"Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006). Machine learning: A review of classification and combining techniques. Artificial Intelligence Review, 26(3), 159\u2013190. https:\/\/doi.org\/10.1007\/s10462-007-9052-3.","journal-title":"Artificial Intelligence Review"},{"issue":"1","key":"9504_CR33","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s10044-002-0173-7","volume":"6","author":"L Kuncheva","year":"2003","unstructured":"Kuncheva, L., Whitaker, C., Shipp, C., & Duin, R. (2003). Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications, 6(1), 22\u201331. https:\/\/doi.org\/10.1007\/s10044-002-0173-7.","journal-title":"Pattern Analysis and Applications"},{"key":"9504_CR34","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1109\/3468.618255","volume":"27","author":"L Lam","year":"1997","unstructured":"Lam, L., & Suen, S. (1997). Application of majority voting to pattern recognition: An analysis of its behavior and performance. IEEE Transactions on Systems, Man, and Cybernetics, 27, 553\u2013567.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"9504_CR35","doi-asserted-by":"publisher","unstructured":"Leon, F., Floria, S. A., & Badica, C. (2017). Evaluating the effect of voting methods on ensemble-based classification. In INISTA-17, (pp. 1\u20136). https:\/\/doi.org\/10.1109\/INISTA.2017.8001122","DOI":"10.1109\/INISTA.2017.8001122"},{"key":"9504_CR36","doi-asserted-by":"crossref","unstructured":"Leung, K. T., & Parker, D. S. (2003). Empirical comparisons of various voting methods in bagging. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 595\u2013600). NY, USA: ACM.","DOI":"10.1145\/956750.956825"},{"key":"9504_CR37","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1016\/S0167-8655(03)00035-7","volume":"24","author":"S Yacoub","year":"2003","unstructured":"Yacoub, S., Lin, X., & Simske, S. (2003). Performance analysis of pattern classifier combination by plurality voting. Pattern Recognition Letters, 24, 1959\u20131969.","journal-title":"Pattern Recognition Letters"},{"key":"9504_CR38","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9760.00128","author":"C List","year":"2001","unstructured":"List, C., & Goodin, R. (2001). Epistemic democracy: Generalizing the condorcet jury theorem. Journal of Political Philosophy. https:\/\/doi.org\/10.1111\/1467-9760.00128.","journal-title":"Journal of Political Philosophy"},{"key":"9504_CR39","doi-asserted-by":"publisher","unstructured":"Loreggia, A., Mattei, N., Rossi, F., & K. Brent Venable. (2018). Preferences and Ethical Principles in Decision Making. In Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society (AIES '18). New York, NY: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3278721.3278723","DOI":"10.1145\/3278721.3278723"},{"key":"9504_CR40","doi-asserted-by":"publisher","unstructured":"Melville, P., Shah, N., Mihalkova, L., & Mooney, R. J. (2004). Experiments on ensembles with missing and noisy data. In Multiple Classifier Systems, 5th International Workshop, MCS 2004, Cagliari, Italy, June 9\u201311, 2004 (pp. 293\u2013302). https:\/\/doi.org\/10.1007\/978-3-540-25966-4_29","DOI":"10.1007\/978-3-540-25966-4_29"},{"issue":"2","key":"9504_CR41","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s11063-009-9097-1","volume":"29","author":"X Mu","year":"2009","unstructured":"Mu, X., Watta, P., & Hassoun, M. H. (2009). Analysis of a plurality voting-based combination of classifiers. Neural Processing Letters, 29(2), 89\u2013107. https:\/\/doi.org\/10.1007\/s11063-009-9097-1.","journal-title":"Neural Processing Letters"},{"issue":"2","key":"9504_CR42","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1007\/s10489-017-0982-4","volume":"48","author":"AF Neto","year":"2018","unstructured":"Neto, A. F., & Canuto, A. M. P. (2018). An exploratory study of mono and multi-objective metaheuristics to ensemble of classifiers. Applied Intelligence, 48(2), 416\u2013431. https:\/\/doi.org\/10.1007\/s10489-017-0982-4.","journal-title":"Applied Intelligence"},{"key":"9504_CR43","unstructured":"Newman, C. B. D., & Merz, C. (1998). UCI repository of machine learning databases. http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html"},{"issue":"2","key":"9504_CR44","doi-asserted-by":"publisher","first-page":"289","DOI":"10.2307\/2526438","volume":"23","author":"S. Nitzan","year":"1982","unstructured":"Nitzan, S., & Paroush, J. (1982). Optimal decision rules in uncertain dichotomous choice situations. International Economic Review, 23(2), 289\u2013297.","journal-title":"International Economic Review"},{"key":"9504_CR45","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.engappai.2016.01.012","volume":"51","author":"I Perikos","year":"2016","unstructured":"Perikos, I., & Hatzilygeroudis, I. (2016). Recognizing emotions in text using ensemble of classifiers. Engineering Applications of Artificial Intelligence, 51, 191\u2013201.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1\u20132","key":"9504_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1\u20132), 1\u201339.","journal-title":"Artificial Intelligence Review"},{"key":"9504_CR47","unstructured":"Rossi, F., & Loreggia, A. (2019). Preferences and ethical priorities: Thinking fast and slow in AI. In Proceedings of the 18th Autonomous Agents and Multi-agent Systems Conference (pp. 3\u20134)."},{"key":"9504_CR48","doi-asserted-by":"publisher","DOI":"10.2200\/S00372ED1V01Y201107AIM014","author":"F Rossi","year":"2011","unstructured":"Rossi, F., Venable, K. B., & Walsh, T. (2011). A short introduction to preferences: Between artificial intelligence and social choice. Synthesis lectures on artificial intelligence and machine learning, morgan & claypool publishers,. https:\/\/doi.org\/10.2200\/S00372ED1V01Y201107AIM014.","journal-title":"Synthesis lectures on artificial intelligence and machine learning, morgan & claypool publishers,"},{"key":"9504_CR49","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.artmed.2017.09.006","volume":"85","author":"E Saleh","year":"2018","unstructured":"Saleh, E., Blaszczynski, J., Moreno, A., Valls, A., Romero-Aroca, P., de la Riva-Fernandez, S., & Slowinski, R. (2018). Learning ensemble classifiers for diabetic retinopathy assessment. Artificial Intelligence in Medicine, 85, 50\u201363. https:\/\/doi.org\/10.1016\/j.artmed.2017.09.006.","journal-title":"Artificial Intelligence in Medicine"},{"key":"9504_CR50","doi-asserted-by":"publisher","unstructured":"Moulin, H. (2016). In F. Brandt, V. Conitzer, U. Endriss, J. Lang, & A. Procaccia (Eds.), Handbook of Computational Social Choice. Cambridge: Cambridge University Press. https:\/\/doi.org\/10.1017\/CBO9781107446984","DOI":"10.1017\/CBO9781107446984"},{"key":"9504_CR51","doi-asserted-by":"crossref","unstructured":"Shapley, L., & Grofman, B. (1984). Optimizing group judgmental accuracy in the presence of interdependencies. Public Choice.","DOI":"10.1007\/BF00118940"},{"key":"9504_CR52","doi-asserted-by":"crossref","unstructured":"Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Vol. 1 (pp. 3645\u20133650). Long Papers.","DOI":"10.18653\/v1\/P19-1355"},{"issue":"8","key":"9504_CR53","doi-asserted-by":"publisher","first-page":"245","DOI":"10.3390\/ijgi6080245","volume":"6","author":"X Sun","year":"2017","unstructured":"Sun, X., Lin, X., Shen, S., & Hu, Z. (2017). High-resolution remote sensing data classification over urban areas using random forest ensemble and fully connected conditional random field. ISPRS International Journal of Geo-Information, 6(8), 245. https:\/\/doi.org\/10.3390\/ijgi6080245.","journal-title":"ISPRS International Journal of Geo-Information"},{"issue":"2","key":"9504_CR54","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1023\/A:1007659514849","volume":"40","author":"GI Webb","year":"2000","unstructured":"Webb, G. I. (2000). Multiboosting: A technique for combining boosting and wagging. Machine Learning, 40(2), 159\u2013196. https:\/\/doi.org\/10.1023\/A:1007659514849.","journal-title":"Machine Learning"},{"issue":"4","key":"9504_CR55","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.2307\/1961757","volume":"82","author":"H Young","year":"1988","unstructured":"Young, H. (1988). Condorcet\u2019s theory of voting. American Political Science Review, 82(4), 1231-1244. https:\/\/doi.org\/10.2307\/1961757","journal-title":"American Political Science Review"}],"container-title":["Autonomous Agents and Multi-Agent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10458-021-09504-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10458-021-09504-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10458-021-09504-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T13:25:53Z","timestamp":1632489953000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10458-021-09504-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,21]]},"references-count":55,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["9504"],"URL":"https:\/\/doi.org\/10.1007\/s10458-021-09504-y","relation":{},"ISSN":["1387-2532","1573-7454"],"issn-type":[{"value":"1387-2532","type":"print"},{"value":"1573-7454","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,21]]},"assertion":[{"value":"28 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"22"}}