Rotation forest for big data
- Juez-Gil, Mario 1
- Arnaiz-González, Álvar 1
- Rodríguez, Juan J. 1
- López-Nozal, Carlos 1
- García-Osorio, César 1
-
1
Universidad de Burgos
info
ISSN: 1566-2535, 1872-6305
Argitalpen urtea: 2021
Alea: 74
Orrialdeak: 39-49
Mota: Artikulua
Beste argitalpen batzuk: Information fusion
Lotura duten proiektuak
Erreferentzia bibliografikoak
- Bello-Orgaz, (2016), Inf. Fusion, 28, pp. 45, 10.1016/j.inffus.2015.08.005
- Chen, (2014), Mobile Netw. Appl., 19, pp. 171, 10.1007/s11036-013-0489-0
- Luengo, (2020)
- C. Moretti, K. Steinhaeuser, D. Thain, N.V. Chawla, Scaling up classifiers to cloud computers, in: 2008 Eighth IEEE International Conference on Data Mining, 2008, pp. 472–481.
- Hashem, (2015), Inf. Syst., 47, pp. 98, 10.1016/j.is.2014.07.006
- Dean, (2008), Commun. ACM, 51, pp. 107, 10.1145/1327452.1327492
- Kuncheva, (2014)
- Rodriguez, (2006), IEEE Trans. Pattern Anal. Mach. Intell., 28, pp. 1619, 10.1109/TPAMI.2006.211
- Kuncheva, (2007), pp. 459
- Ghemawat, (2003), pp. 29
- Zaharia, (2012), pp. 15
- Zaharia, (2010), HotCloud, 10, pp. 95
- Meng, (2016), J. Mach. Learn. Res., 17, pp. 1235
- M. Assefi, E. Behravesh, G. Liu, A.P. Tafti, Big data machine learning using Apache Spark MLlib, in: 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 3492–3498.
- (2011)
- L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, G. Varoquaux, API design for machine learning software: experiences from the scikit-learn project, in: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 2013, pp. 108–122.
- Cover, (1967), IEEE Trans. Inform. Theory, 13, pp. 21, 10.1109/TIT.1967.1053964
- Fix, (1951)
- Maillo, (2017), Knowl.-Based Syst., 117, pp. 3, 10.1016/j.knosys.2016.06.012
- Ramírez-Gallego, (2017), IEEE Trans. Syst. Man Cybern. Syst., 47, pp. 2727, 10.1109/TSMC.2017.2700889
- Tyree, (2011), pp. 387
- Chen, (2017), IEEE Trans. Parallel Distrib. Syst., 28, pp. 919, 10.1109/TPDS.2016.2603511
- J. Gonzalez-Lopez, A. Cano, S. Ventura, Large-scale multi-label ensemble learning on Spark, in: 2017 IEEE Trustcom/BigDataSE/ICESS, 2017, pp. 893–900.
- García-Gil, (2018), Knowl.-Based Syst., 150, pp. 166, 10.1016/j.knosys.2018.03.012
- Friedman, (1997), Mach. Learn., 29, pp. 131, 10.1023/A:1007465528199
- Arias, (2017), Knowl.-Based Syst., 117, pp. 16, 10.1016/j.knosys.2016.06.013
- García, (2016), Big Data Anal., 1, pp. 9, 10.1186/s41044-016-0014-0
- Ramírez-Gallego, (2018), Swarm Evol. Comput., 38, pp. 240, 10.1016/j.swevo.2017.08.005
- García-Gil, (2019), Inform. Sci., 479, pp. 135, 10.1016/j.ins.2018.12.002
- Ramírez-Gallego, (2018)
- Arnaiz-González, (2016), Knowl.-Based Syst., 107, pp. 83, 10.1016/j.knosys.2016.05.056
- Arnaiz-González, (2017), Progress in Artificial Intelligence, 6, pp. 211, 10.1007/s13748-017-0117-5
- García-Osorio, (2010), Artificial Intelligence, 174, pp. 410, 10.1016/j.artint.2010.01.001
- Breiman, (2001), Mach. Learn., 45, pp. 5, 10.1023/A:1010933404324
- Fernández-Delgado, (2014), J. Mach. Learn. Res., 15, pp. 3133
- Breiman, (1996), Mach. Learn., 24, pp. 123, 10.1007/BF00058655
- Bagnall, (2018)
- Pardo, (2013), Appl. Math. Comput., 219, pp. 9914
- Zhang, (2008), Pattern Recognit. Lett., 29, pp. 1524, 10.1016/j.patrec.2008.03.006
- Fernández, (2017), Complex Intell. Syst., 3, pp. 105, 10.1007/s40747-017-0037-9
- Ramírez-Gallego, (2018), Inf. Fusion, 42, pp. 51, 10.1016/j.inffus.2017.10.001
- Stiglic, (2011), pp. 211
- B. Panda, J.S. Herbach, S. Basu, R.J. Bayardo, PLANET: Massively parallel learning of tree ensembles with MapReduce, in: Proceedings of the 35th International Conference on Very Large Data Bases (VLDB-2009), 2009.
- Dua, (2017)
- Benavoli, (2017), J. Mach. Learn. Res., 18, pp. 2653
- Corani, (2015), Mach. Learn., 100, pp. 285, 10.1007/s10994-015-5486-z
- Krawczyk, (2017), Inf. Fusion, 37, pp. 132, 10.1016/j.inffus.2017.02.004
- Díez-Pastor, (2015), Inform. Sci., 325, pp. 98, 10.1016/j.ins.2015.07.025
Atariko dokumentuak egunero eguneratzen dira. Data horrek atariaren egiturari buruzko informazioa eguneratzeari egiten dio erreferentzia (pertsonak, ikerketa-taldeak, antolaketa-unitateak, proiektuak...).