A Scalable Expressive Ensemble Learning Using Random Prism: A MapReduce Approach - Transactions on Large-Scale-Data-and Knowledge-Centered Systems XX - page 107

17. Panda, B., Herbach, J.S., Basu, S., Bayardo, R.J.: Planet: massively parallel learn-
ing of tree ensembles with mapreduce. Proc. VLDB Endow. 2 , 1426-1437 (2009)
18. Quinlan, R.J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San
Francisco (1993)
19. Stahl, F., Bramer, M.: Computationally ecient induction of classification rules
with the PMCRI and J-PMCRI frameworks. Knowl.-Based Syst. 35 , 49-63 (2012)
20. Stahl, F., Bramer, M.: Random prism: a noise-tolerant alternative to random
forests. Expert Syst. 31 (4), 411-420 (2013)
21. Stahl, F., Bramer, M., Adda, M.: Parallel rule induction with information theoretic
pre-pruning. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Develop-
ment in Intelligent Systems XXVI, pp. 151-164. Springer, London (2010)
22. Stahl, F., May, D., Bramer, M.: Parallel random prism: a computationally ecient
ensemble learner for classification. In: Bramer, M., Petridis, M. (eds.) Research and
Development in Intelligent Systems XXIX, pp. 21-34. Springer, London (2012)
23. Tlili, R., Slimani, Y.: A hierarchical dynamic load balancing strategy for distributed
data mining. Int. J. Adv. Sci. Technol. 39 , 29-48 (2012)
24. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Tech-
niques: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann
Series in Data Management Systems. Elsevier Science, Amsterdam (2011)