{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T15:17:33Z","timestamp":1758122253475,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,5]],"date-time":"2020-08-05T00:00:00Z","timestamp":1596585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,5]]},"DOI":"10.1145\/3421558.3421581","type":"proceedings-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T20:31:31Z","timestamp":1606422691000},"page":"140-145","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Label Aggregation of Gradient Boosting Decision Trees"],"prefix":"10.1145","author":[{"given":"Xingchun","family":"Xiang","sequence":"first","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, China"}]},{"given":"Huaixuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, China"}]},{"given":"Shu-Tao","family":"Xia","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, China"}]}],"member":"320","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022643204877"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1018054314350"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1006\/inco.1995.1136"},{"key":"e_1_3_2_1_5_1","volume-title":"boosting, and C4. 5. City","author":"Quinlan J. R.","year":"1996","unstructured":"Quinlan , J. R. Bagging , boosting, and C4. 5. City , 1996 . Quinlan, J. R. Bagging, boosting, and C4. 5. City, 1996."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-59119-2_166"},{"key":"e_1_3_2_1_8_1","volume-title":"Greedy function approximation: a gradient boosting machine. Annals of statistics","author":"Friedman J. H.","year":"2001","unstructured":"Friedman , J. H. Greedy function approximation: a gradient boosting machine. Annals of statistics ( 2001 ), 1189-1232. Friedman, J. H. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189-1232."},{"key":"e_1_3_2_1_9_1","volume-title":"City","author":"Chen T.","year":"2016","unstructured":"Chen , T. and Guestrin , C . Xgboost: A scalable tree boosting system . City , 2016 . Chen, T. and Guestrin, C. Xgboost: A scalable tree boosting system. City, 2016."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_3_2_1_11_1","series-title":"Springer series in statistics New York","volume-title":"The elements of statistical learning","author":"Friedman J.","year":"2001","unstructured":"Friedman , J. , Hastie , T. and Tibshirani , R . The elements of statistical learning . Springer series in statistics New York , 2001 . Friedman, J., Hastie, T. and Tibshirani, R. The elements of statistical learning. Springer series in statistics New York, 2001."},{"key":"e_1_3_2_1_12_1","volume-title":"Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7","author":"Natekin A.","year":"2013","unstructured":"Natekin , A. and Knoll , A . Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7 ( 2013 ), 21. Natekin, A. and Knoll, A. Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7 (2013), 21."},{"key":"e_1_3_2_1_13_1","volume-title":"International journal of forecasting, 30, 2","author":"Taieb S. B.","year":"2014","unstructured":"Taieb , S. B. and Hyndman , R. J . A gradient boosting approach to the Kaggle load forecasting competition . International journal of forecasting, 30, 2 ( 2014 ), 382-394. Taieb, S. B. and Hyndman, R. J. A gradient boosting approach to the Kaggle load forecasting competition. International journal of forecasting, 30, 2 (2014), 382-394."},{"key":"e_1_3_2_1_14_1","volume-title":"City","author":"Zheng Z.","year":"2007","unstructured":"Zheng , Z. , Chen , K. , Sun , G. and Zha , H . A regression framework for learning ranking functions using relative relevance judgments . City , 2007 . Zheng, Z., Chen, K., Sun, G. and Zha, H. A regression framework for learning ranking functions using relative relevance judgments. City, 2007."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383129"},{"key":"e_1_3_2_1_16_1","volume-title":"City","author":"Hutchinson R. A.","year":"2011","unstructured":"Hutchinson , R. A. , Liu , L.-P. and Dietterich , T. G . Incorporating boosted regression trees into ecological latent variable models . City , 2011 . Hutchinson, R. A., Liu, L.-P. and Dietterich, T. G. Incorporating boosted regression trees into ecological latent variable models. City, 2011."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-010-9156-z"},{"key":"e_1_3_2_1_18_1","volume-title":"City","author":"Brodley C. E.","year":"1996","unstructured":"Brodley , C. E. and Friedl , M. A . Identifying and eliminating mislabeled training instances . City , 1996 . Brodley, C. E. and Friedl, M. A. Identifying and eliminating mislabeled training instances. City, 1996."},{"key":"e_1_3_2_1_19_1","volume-title":"City","author":"Guyon I.","year":"1996","unstructured":"Guyon , I. , Matic , N. and Vapnik , V . Discovering Informative Patterns and Data Cleaning . City , 1996 . Guyon, I., Matic, N. and Vapnik, V. Discovering Informative Patterns and Data Cleaning. City, 1996."},{"key":"e_1_3_2_1_20_1","volume-title":"City","author":"Hern\u00e1ndez-Lobato D.","year":"2011","unstructured":"Hern\u00e1ndez-Lobato , D. , Hern\u00e1ndez-Lobato , J. M. and Dupont , P . Robust multi-class Gaussian process classification . City , 2011 . Hern\u00e1ndez-Lobato, D., Hern\u00e1ndez-Lobato, J. M. and Dupont, P. Robust multi-class Gaussian process classification. City, 2011."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti738"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214507000000617"},{"key":"e_1_3_2_1_23_1","first-page":"1","article-title":"Classification Performance Comparison of Feature Vectors Based on Summation Scheme and Maximization Scheme","volume":"1","author":"Yu H","year":"2011","unstructured":"Yu , H . Classification Performance Comparison of Feature Vectors Based on Summation Scheme and Maximization Scheme . International Journal of Machine Learning and Computing , 1 , 1 ( 2011 ), 73. Yu, H. Classification Performance Comparison of Feature Vectors Based on Summation Scheme and Maximization Scheme. International Journal of Machine Learning and Computing, 1, 1 (2011), 73.","journal-title":"International Journal of Machine Learning and Computing"},{"key":"e_1_3_2_1_24_1","volume-title":"City","author":"Biggio B.","year":"2011","unstructured":"Biggio , B. , Nelson , B. and Laskov , P . Support vector machines under adversarial label noise . City , 2011 . Biggio, B., Nelson, B. and Laskov, P. Support vector machines under adversarial label noise. City, 2011."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18178\/ijmlc.2017.7.6.641"},{"key":"e_1_3_2_1_26_1","first-page":"4","article-title":"An efficient classification approach for data mining","volume":"2","author":"Parashar H. J.","year":"2012","unstructured":"Parashar , H. J. , Vijendra , S. and Vasudeva , N . An efficient classification approach for data mining . International Journal of Machine Learning and Computing , 2 , 4 ( 2012 ), 446. Parashar, H. J., Vijendra, S. and Vasudeva, N. An efficient classification approach for data mining. International Journal of Machine Learning and Computing, 2, 4 (2012), 446.","journal-title":"International Journal of Machine Learning and Computing"},{"key":"e_1_3_2_1_27_1","first-page":"2","article-title":"Multiple Classifiers Approach based on Dynamic Selection to Maximize Classification Performance","volume":"1","author":"Ayad O.","year":"2011","unstructured":"Ayad , O. and Syed-Mouchaweh , M . Multiple Classifiers Approach based on Dynamic Selection to Maximize Classification Performance . International Journal of Machine Learning and Computing , 1 , 2 ( 2011 ), 154. Ayad, O. and Syed-Mouchaweh, M. Multiple Classifiers Approach based on Dynamic Selection to Maximize Classification Performance. International Journal of Machine Learning and Computing, 1, 2 (2011), 154.","journal-title":"International Journal of Machine Learning and Computing"},{"key":"e_1_3_2_1_28_1","volume-title":"Pattern Generation through Feature Values Modification and Decision Tree Ensemble Construction","author":"Akhand M.","year":"2013","unstructured":"Akhand , M. , Rahman , M. and Murase , K . Pattern Generation through Feature Values Modification and Decision Tree Ensemble Construction ( 2013 ). Akhand, M., Rahman, M. and Murase, K. Pattern Generation through Feature Values Modification and Decision Tree Ensemble Construction (2013)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007682208299"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2005.06.070"},{"key":"e_1_3_2_1_31_1","volume-title":"The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nature reviews cancer, 8, 1","author":"Clarke R.","year":"2008","unstructured":"Clarke , R. , Ressom , H. W. , Wang , A. , Xuan , J. , Liu , M. C. , Gehan , E. A. and Wang , Y . The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nature reviews cancer, 8, 1 ( 2008 ), 37-49. Clarke, R., Ressom, H. W., Wang, A., Xuan, J., Liu, M. C., Gehan, E. A. and Wang, Y. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nature reviews cancer, 8, 1 (2008), 37-49."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-0742-7_8"},{"key":"e_1_3_2_1_33_1","volume-title":"City","author":"Asuncion A.","year":"2007","unstructured":"Asuncion , A. and Newman , D . UCI machine learning repository . City , 2007 . Asuncion, A. and Newman, D. UCI machine learning repository. City, 2007."}],"event":{"name":"IPMV 2020: 2020 2nd International Conference on Image Processing and Machine Vision","acronym":"IPMV 2020","location":"Bangkok Thailand"},"container-title":["2020 2nd International Conference on Image Processing and Machine Vision"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3421558.3421581","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3421558.3421581","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:45Z","timestamp":1750200105000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3421558.3421581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,5]]},"references-count":33,"alternative-id":["10.1145\/3421558.3421581","10.1145\/3421558"],"URL":"https:\/\/doi.org\/10.1145\/3421558.3421581","relation":{},"subject":[],"published":{"date-parts":[[2020,8,5]]},"assertion":[{"value":"2020-11-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}