MRPR: A MapReduce solution for prototype reduction in big data classification

Neurocomputing

Volumn 150, Issue Part A, 2015, Pages 331-345

  Triguero, Isaac ^a   Peralta, Daniel ^a   Bacardit, Jaume ^b   García, Salvador ^c   Herrera, Francisco ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b NEWCASTLE UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF JAÉN   (Spain)

Author keywords

Big data; Hadoop; Mahout; Nearest neighbor classification; Prototype generation; Prototype reduction

Indexed keywords

BIG DATA; CLASSIFICATION (OF INFORMATION); CLUSTER COMPUTING; DATA MINING; DIGITAL STORAGE;

DATA CLASSIFICATION PROBLEMS; DISTRIBUTED PARTITIONING; HADOOP; MAHOUT; NEAREST NEIGHBOR CLASSIFICATION; NEAREST NEIGHBOR CLASSIFIER; PROTOTYPE GENERATIONS; PROTOTYPE REDUCTION ALGORITHMS;

DATA REDUCTION;

ALGORITHM; ARTICLE; CLASSIFIER; COMPUTER ANALYSIS; DATA ANALYSIS; DATA MINING; DATA PROCESSING; K NEAREST NEIGHBOR; MAPREDUCE FOR PROTOTYPE REDUCTION;

EID: 84912100031     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.04.078     Document Type: Article

References (55)

1. Marx V. The big challenges of big data. Nature 2013, 498(7453):255-260.
2. Minelli M., Chambers M., Dhiraj A. Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses (Wiley CIO) 2013, Wiley Publishing. 1st edition.
3. D. Plummer, T. Bittman, T. Austin, D. Cearley, D.S. Cloud, Defining and Describing an Emerging Phenomenon, Technical Report, Gartner, 2008.
4. Alpaydin E. Introduction to Machine Learning 2010, MIT Press, Cambridge, MA. 2nd edition.
5. Woniak M., Graña M., Corchado E. A survey of multiple classifier systems as hybrid systems. Inf. Fusion 2014, 16:3-17.
6. Sakr S., Liu A., Batista D., Alomari M. A survey of large scale data management approaches in cloud environments. IEEE Commun. Surv. Tutor. 2011, 13(3):311-336.
7. Bacardit J., Llorà X. Large-scale data mining using genetics-based machine learning. Wiley Interdiscipl. Rev.: Data Min. Knowl. Discov. 2013, 3(1):37-61.
8. Dean J., Ghemawat S. Map reduce. simplified data processing on large clusters. Commun. ACM 2008, 51(1):107-113.
9. Dean J., Ghemawat S. Map reduce. a flexible data processing tool. Commun. ACM 2010, 53(1):72-77.
10. S. Ghemawat, H. Gobioff, S.-T. Leung, The google file system, in: Proceedings of the nineteenth ACM symposium on Operating systems principles, SOSP'03, 2003, pp. 29-43.
11. Snir M., Otto S. MPI-The Complete Reference: The MPI Core 1998, MIT Press, Cambridge, Massachusetts.
12. Zhao W., Ma H., He Q. Parallel k-means clustering based on mapreduce. Cloud Computing Lecture Notes in Computer Science 2009, vol. 5931:674-679. Springer, Berlin, Heidelberg.
13. Srinivasan A., Faruquie T., Joshi S. Data and task parallelism in ILP using mapreduce. Mach. Learn. 2012, 86(1):141-168.
14. He Q., Du C., Wang Q., Zhuang F., Shi Z. A parallel incremental extreme svm classifier. Neurocomputing 2011, 74(16):2532-2540.
15. Palit I., Reddy C. Scalable and parallel boosting with mapreduce. IEEE Trans. Knowl. Data Eng. 2012, 24(10):1904-1916.
16. Caruana G., Li M., Liu Y. An ontology enhanced parallel SVM for scalable spam filter training. Neurocomputing 2013, 108:45-57.
17. Pyle D. Data Preparation for Data Mining, The Morgan Kaufmann Series in Data Management Systems 1999, Morgan Kaufmann, San Francisco, USA.
18. Computational Methods of Feature Selection 2007, Chapman & Hall/Crc Data Mining and Knowledge Discovery Series Chapman & Hall/Crc, Boca Raton, FL, USA. H. Liu, H. Motoda (Eds.).
19. García S., Derrac J., Cano J., Herrera F. Prototype selection for nearest neighbor classification. taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34(3):417-435.
20. Triguero I., Derrac J., García S., Herrera F. A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Trans. Syst., Man, Cybern. Part C. Appl. Rev. 2012, 42(1):86-100.
21. Derrac J., García S., Herrera F. IFS-CoCo. instance and feature selection based on cooperative coevolution with nearest neighbor rule. Pattern Recognit. 2010, 43(6):2082-2105.
22. Derrac J., Cornelis C., García S., Herrera F. Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Inf. Sci. 2012, 186(1):73-92.
23. García-Pedrajas N., de Haro-García A., Pérez-Rodríguez J. A scalable approach to simultaneous evolutionary instance and feature selection. Inf. Sci. 2013, 228:150-174.
24. Cover T.M., Hart P.E. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 1967, 13(1):21-27.
25. Nanni L., Lumini A. Particle swarm optimization for prototype reduction. Neurocomputing 2008, 72(4-6):1092-1097.
26. Triguero I., García S., Herrera F. IPADE. iterative prototype adjustment for nearest neighbor classification. IEEE Trans. Neural Netw. 2010, 21(12):1984-1990.
27. Triguero I., García S., Herrera F. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognit. 2011, 44(4):901-916.
28. Cano J.R., Herrera F., Lozano M. Stratification for scaling up evolutionary prototype selection. Pattern Recognit. Lett. 2005, 26(7):953-963.
29. Derrac J., García S., Herrera F. Stratified prototype selection based on a steady-state memetic algorithm. a study of scalability. Memet. Comput. 2010, 2(3):183-199.
30. I. Triguero, J. Derrac, S. García, F. Herrera, A study of the scaling up capabilities of stratified prototype generation, in: Proceedings of the third World Congress on Nature and Biologically Inspired Computing (NABIC'11), 2011, pp. 304-309.
31. Chen W.-Y., Song Y., Bai H., Lin C.-J., Chang E. Parallel spectral clustering in distributed systems. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(3):568-586.
32. García S., Cano J.R., Herrera F. A memetic algorithm for evolutionary prototype selection. a scaling up approach. Pattern Recognit. 2008, 41(8):2693-2709.
33. García-Pedrajas N., Pérez-Rodríguez J. Multi-selection of instances. a straightforward way to improve evolutionary instance selection. Appl. Soft Comput. 2012, 12(11):3590-3602.
34. B. He, W. Fang, Q. Luo, N.K. Govindaraju, T. Wang, Mars: a mapreduce framework on graphics processors, in: Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques, PACT'08, ACM, New York, NY, USA, 2008, pp. 260-269.
35. J. Talbot, R.M. Yoo, C. Kozyrakis, Phoenix++: modular mapreduce for shared-memory systems, in: Proceedings of the Second International Workshop on MapReduce and Its Applications, ACM, New York, NY, USA, 2011, pp. 9-16, doi:. doi:10.1145/1996092.1996095.
36. White T. Hadoop: The Definitive Guide 2012, O'Reilly Media, Inc., Sebastopol, CA, USA. 3rd edition.
37. A.H. Project, Apache hadoop, 2013, http://hadoop.apache.org/.
38. A.M. Project, Apache mahout, 2013, http://mahout.apache.org/.
39. Chang C.-L. Finding prototypes for nearest neighbor classifiers. IEEE Trans. Comput. 1974, 23(11):1179-1184.
40. C.-T. Chu, S. Kim, Y.-A. Lin, Y. Yu, G. Bradski, A. Ng, K. Olukotun, Map-reduce for machine learning on multicore, in: Advances in Neural Information Processing Systems, 2007, pp. 281-288.
41. Wilson D.L. Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst., Man Cybern. 1972, 2(3):408-421.
42. Hart P.E. The condensed nearest neighbor rule. IEEE Trans. Inf. Theory 1968, 18:515-516.
43. Wilson D.R., Martinez T.R. Reduction techniques for instance-based learning algorithms. Mach. Learn. 2000, 38(3):257-286.
44. Mollineda R., Ferri F., Vidal E. A merge-based condensing strategy for multiple prototype classifiers. IEEE Trans. Syst., Man Cybern. B 2002, 32(5):662-668.
45. Sánchez J.S. High training set size reduction by space partitioning and prototype abstraction. Pattern Recognit. 2004, 37(7):1561-1564.
46. Sánchez J.S., Barandela R., Marqués A.I., Alejo R., Badenas J. Analysis of new techniques to obtain quality training sets. Pattern Recognit. Lett. 2003, 24(7):1015-1022.
47. Lam W., Keung C.K., Liu D. Discovering useful concept prototypes for classification based on filtering and abstraction. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 14(8):1075-1090.
48. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco. 2nd edition.
49. G.M. Amdahl, Validity of the single processor approach to achieving large scale computing capabilities, in: Proceedings of the Spring Joint Computing Conference, ACM, New York, NY, USA, 1967, pp. 483-485.
50. Cloudera, Cloudera Distribution Including apache hadoop, 2013, http://www.cloudera.com.
51. A. Frank, A. Asuncion, UCI Machine Learning Repository, 2010, 〈http://archive.ics.uci.edu/mlhttp://archive.ics.uci.edu/ml〉.
52. Kohonen T. The self organizing map. Proc. IEEE 1990, 78(9):1464-1480.
53. Angiulli F. Fast nearest neighbor condensation for large data sets classification. IEEE Trans. Knowl. Data Eng. 2007, 19(11):1450-1464.
54. Price K.V., Storn R.M., Lampinen J.A. Differential Evolution A Practical Approach to Global Optimization. Natural Computing Series 2005, ISBN 978-3-540-31306-9.
55. Neri F., Tirronen V. Scale factor local search in differential evolution. Memet. Comput. 2009, 1(2):153-171.