Parallel random prism: A computationally efficient ensemble learner for classification

Res. and Dev. in Intelligent Syst. XXIX: Incorporating Applications and Innovations in Intel. Sys. XX - AI 2012, 32nd SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel.

Volumn , Issue , 2012, Pages 21-34

  Stahl, Frederic ^a   May, David ^b   Bramer, Max ^b  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTER COMPUTING; DECISION TREES; FORESTRY; LARGE DATASET; LEARNING SYSTEMS; PRISMS;

CLASSIFICATION ACCURACY; CLASSIFICATION RULES; COMPUTATIONAL CHALLENGES; COMPUTATIONAL OVERHEADS; COMPUTATIONALLY EFFICIENT; COMPUTING CLUSTERS; ENSEMBLE LEARNING APPROACH; MULTIPLE BASE CLASSIFIERS;

CLASSIFICATION (OF INFORMATION);

ALGORITHMS; CLASSIFICATION; FORESTRY; INFORMATION RETRIEVAL; PRISMS;

EID: 84881484792     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-1-4471-4739-8_2     Document Type: Conference Paper

References (30)

1. Hadoop, http://hadoop.apache.org/mapreduce/ 2011
2. Jaume Bacardit and Natalio Krasnogor. The infobiotics PSP benchmarks repository. Technical report, 2008
3. Justin D. Basilico, M. Arthur Munson, Tamara G. Kolda, Kevin R. Dixon, and W. Philip Kegelmeyer. Comet: A recipe for learning and using large ensembles on massive data. CoRR, abs/1103.2068, 2011
4. C L Blake and C J Merz. UCI repository of machine learning databases. Technical report, University of California, Irvine, Department of Information and Computer Sciences, 1998
5. M A Bramer. Automatic induction of classification rules from examples using N-Prism. In Research and Development in Intelligent Systems XVI, pages 99-121, Cambridge, 2000. Springer-Verlag
6. M A Bramer. An information-theoretic approach to the pre-pruning of classification rules. In B Neumann M Musen and R Studer, editors, Intelligent Information Processing, pages 201-212. Kluwer, 2002
7. M A Bramer. Inducer: A public domain workbench for data mining. International Journal of Systems Science, 36(14):909-919, 2005
8. Leo Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996
9. Leo Breiman. Random forests. Machine Learning, 45(1):5-32, 2001
10. J. Cendrowska. PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies, 27(4):349-370, 1987
11. Philip Chan and Salvatore J Stolfo. Experiments on multistrategy learning by meta learning. In Proc. Second Intl. Conference on Information and Knowledge Management, pages 314-323, 1993
12. Philip Chan and Salvatore J Stolfo. Meta-Learning for multi strategy and parallel learning. In Proceedings. Second International Workshop on Multistrategy Learning, pages 150-165, 1993
13. B.V. Dasarathy and B.V. Sheela. A composite classifier system design: Concepts and methodology. Proceedings of the IEEE, 67(5):708-713, 1979
14. Jeffrey Dean and Sanjay Ghemawat. Mapreduce: Simplified data processing on large clusters. Commun. ACM, 51:107-113, January 2008
15. Pedro Domingos and Geoff Hulten. Mining high-speed data streams. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '00, pages 71-80, New York, NY, USA, 2000. ACM
16. J Fuernkranz. Integrative windowing. Journal of Artificial Intelligence Resarch, 8:129-164, 1998
17. John L Hennessy and David A Patterson. Computer Architecture A Quantitative Approach. Morgan Kaufmann, USA, third edition, 2003
18. Tin Kam Ho. Random decision forests. Document Analysis and Recognition, International Conference on, 1:278, 1995
19. Nan-Chen Hsieh and Lun-Ping Hung. A data driven ensemble classifier for credit scoring analysis. Expert Systems with Applications, 37(1):534 -545, 2010
20. Kai Hwang and Fay A Briggs. Computer Architecture and Parallel Processing. McGraw-Hill Book Co., international edition, 1987
21. Biswanath Panda, Joshua S. Herbach, Sugato Basu, and Roberto J. Bayardo. Planet: Massively parallel learning of tree ensembles with mapreduce. Proc. VLDB Endow., 2:1426-1437, August 2009
22. Ross J Quinlan. Induction of decision trees. Machine Learning, 1(1):81-106, 1986
23. Ross J Quinlan. C4.5: Programs for machine learning. Morgan Kaufmann, 1993
24. Lior Rokach. Ensemble-based classifiers. Artificial Intelligence Review, 33:1-39, 2010
25. F. Stahl, M.M. Gaber, M. Bramer, and P.S. Yu. Pocket data mining: Towards collaborative data mining in mobile computing environments. In 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), volume 2, pages 323 -330, October 2010
26. Frederic Stahl and Max Bramer. Random Prism: An alternative to random forests. In Thirtyfirst SGAI International Conference on Artificial Intelligence, pages 5-18, Cambridge, England, 2011
27. Frederic Stahl, Mohamed Gaber, Paul Aldridge, David May, Han Liu, Max Bramer, and Philip Yu. Homogeneous and heterogeneous distributed classification for pocket data mining. In Transactions on Large-Scale Data-and Knowledge-Centered Systems V, volume 7100 of Lecture Notes in Computer Science, pages 183-205. Springer Berlin / Heidelberg, 2012
28. Ian HWitten and Frank Eibe. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, second edition, 2005
29. Gongqing Wu, Haiguang Li, Xuegang Hu, Yuanjun Bi, Jing Zhang, and Xindong Wu. Mrec4.5: C4.5 ensemble classification with mapreduce. In ChinaGrid Annual Conference, 2009. ChinaGrid '09. Fourth, pages 249 -255, 2009
30. Jiang Wu, Meng-Long Li, Le-Zheng Yu, and Chao Wang. An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition. The Protein Journal, 29:62-67, 2010