References
- AlSabti, K., Ranka, S. and Singh, V. (1998). Clouds: Classification for large or out-of-core datasets, Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 2-8.
- Amado, N., Gama, J. and Silva, F. (2001). Parallel implementation of decision tree learning algorithms, in P.10.1007/3-540-45329-6_4
- Brazdil and A. Jorge (Eds.), Progress in Artificial Intelligence, Springer, Berlin/Heidelberg, pp. 6-13.
- Amado, N., Gama, J. and Silva, F. (2003). Exploiting parallelism in decision tree induction, ECML/PKDDWorkshop on Parallel and Distributed Computing for Machine Learning, Cavtat/Dubrovnik, Croatia, pp. 13-22.
- Andrzejak, A., Langner, F. and Zabala, S. (2013). Interpretable models from distributed data via merging of decision trees, IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Savannah, GA, USA, pp. 1-9.
- Bekkerman, R., Bilenko, M. and Langford, J. (2011). Scaling up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, Cambridge.
- Ben-Haim, Y. and Tom-Tov, E. (2010). A streaming parallel decision tree algorithm, The Journal of Machine Learning Research 11: 849-872.
- Breiman, L. (1999). Pasting small votes for classification in large databases and on-line, Machine Learning 36(1-2): 85-103.10.1023/A:1007563306331
- Dai, W. and Ji, W. (2014). A MAPREDUCE implementation of c4.5 decision tree algorithm, International Journal of Database Theory and Application 7(1): 49-60.
- DeWitt, D.J., Naughton, J.F. and Schneider, D. (1991). Parallel sorting on a shared-nothing architecture using probabilistic splitting, Proceedings of the 1st International Conference on Parallel and Distributed Information Systems, Miami Beach, FL, USA, pp. 280-291.
- Domingos, P. and Hulten, G. (2000). Mining high-speed data streams, Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, pp. 71-80.
- Fan, W. and Bifet, A. (2013). Mining big data: Current status, and forecast to the future, ACM sIGKDD Explorations Newsletter 14(2): 1-5.
- Gehrke, J., Ganti, V., Ramakrishnan, R. and Loh, W.-Y. (1999). Boat-optimistic decision tree construction, in S. Davidson and C. Faloutsos (Eds.), ACM SIGMOD Record, Vol. 28, ACM, New York, NY, pp. 169-180.10.1145/304181.304197
- Goil, S. and Choudhary, A. (2001). Parsimony: An infrastructure for parallel multidimensional analysis and data mining, Journal of Parallel and Distributed Computing 61(3): 285-321.10.1006/jpdc.2000.1691
- Hansen, L.K. and Salamon, P. (1990). Neural network ensembles, IEEE Transactions on Pattern Analysis & Machine Intelligence 12(10): 993-1001.
- Jin, R. and Agrawal, G. (2003). Communication and memory efficient parallel decision tree construction, Proceedings of the 3rd SIAM International Conference on Data Mining, San Francisco, CA, USA, pp. 119-129.
- Joshi, M.V., Karypis, G. and Kumar, V. (1998). SCALPARC: A new scalable and efficient parallel classification algorithm for mining large datasets, Parallel Processing Symposium, Los Alamitos, CA, USA, pp. 573-579.
- Kargupta, H. and Park, B.-H. (2004). A Fourier spectrum-based approach to represent decision trees for mining data streams in mobile environments, IEEE Transactions on Knowledge and Data Engineering 16(2): 216-229.
- Kourtellis, N., Morales, G.D.F., Bifet, A. and Murdopo, A. (2016). VHT: Vertical Hoeffding tree, arXiv preprint, 1607.08325.
- Louppe, G. and Geurts, P. (2012). Ensembles on random patches, in P.A. Flach et al. (Eds.), Machine Learning and Knowledge Discovery in Databases, Springer, Berlin/Heidelberg, pp. 346-361.
- Mehta, M., Agrawal, R. and Rissanen, J. (1996). SLIQ: A fast scalable classifier for data mining, in P. Aspers et al. (Eds.), Advances in Database Technology, Springer, Berlin/Heidelberg, pp. 18-32.
- Miglio, R. and Soffritti, G. (2004). The comparison between classification trees through proximity measures, Computational Statistics & Data Analysis 45(3): 577-593.10.1016/S0167-9473(03)00063-X
- Narlikar, G.J. (1998). A parallel, multithreaded decision tree builder, Technical report, DTIC Document, http://www.dtic.mil/docs/citations/ADA363531
- Ntoutsi, I., Kalousis, A. and Theodoridis, Y. (2008). A general framework for estimating similarity of datasets and decision trees: Exploring semantic similarity of decision trees, in C. Apte et al. (Eds.), SIAM Conference on Data Mining, SIAM, Philadelphia, PA, pp. 810-821.
- Panda, B., Herbach, J.S., Basu, S. and Bayardo, R.J. (2009). Planet: Massively parallel learning of tree ensembles with MapReduce, Proceedings of the VLDB Endowment 2(2): 1426-1437.
- Pawlik, M. and Augsten, N. (2011). RTED: A robust algorithm for the tree edit distance, Proceedings of the VLDB Endowment 5(4): 334-345.
- Shafer, J., Agrawal, R. and Mehta, M. (1996). Sprint: A scalable parallel classifier for data mining, International Conference on Very Large Data Bases, Mumbai (Bombay), India, pp. 544-555.
- Shannon, W.D. and Banks, D. (1999). Combining classification trees using MLE, Statistics in Medicine 18(6): 727-740.10.1002/(SICI)1097-0258(19990330)18:6<;727::AID-SIM61>3.0.CO;2-2
- Sollich, P. and Krogh, A. (1996). Learning with ensembles: How overfitting can be useful, in D.S. Touretzky et al. (Eds.)Advances in Neural Information Processing Systems 8, MIT Press, Cambridge, MA, pp. 190-196.
- Sreenivas, M.K., AlSabti, K. and Ranka, S. (2000). Parallel out-of-core decision tree classifiers, in H. Kargupta and P. Chan (Eds.), Advances in Distributed and Parallel Knowledge Discovery, Cambridge, MA, pp. 317-336.
- Srivastava, A., Han, E.-H., Kumar, V. and Singh, V. (1995). Parallel formulations of decision-tree classification algorithms, Data Mining and Knowledge Discovery 3(3): 237-261.
- Triguero, I., Peralta, D., Bacardit, J., Garc´ıa, S. and Herrera, F. (2015). MRPR: A MAPREDUCE solution for prototype reduction in big data classification, Neurocomputing 150(A): 331-345.
- Zhang, K. and Shasha, D. (1989). Simple fast algorithms for the editing distance between trees and related problems, SIAM Journal on Computing 18(6): 1245-1262.10.1137/0218082
- Zhang, X. and Jiang, S. (2012). A splitting criteria based on similarity in decision tree learning, Journal of Software 7(8): 1775-1782.
- Zhang, Y., Gao, Q., Gao, L. and Wang, C. (2012). IMAPREDUCE: A distributed computing framework for iterative computation, Journal of Grid Computing 10(1): 47-68.10.1007/s10723-012-9204-9