Rotation forest for big data

  1. Arnaiz-González, Álvar
  2. Rodríguez, Juan J.
  3. Juez-Gil, Mario
  4. García-Osorio, César
  5. López-Nozal, Carlos
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Revista:
Information fusion

ISSN: 1566-2535 1872-6305

Any de publicació: 2021

Volum: 74

Pàgines: 39-49

Tipus: Article

DOI: 10.1016/J.INFFUS.2021.03.007 GOOGLE SCHOLAR lock_openAccés obert editor

Altres publicacions en: Information fusion

Objetivos de desarrollo sostenible

Referències bibliogràfiques

  • 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