Confidence ratio affinity propagation in ensemble selection of neural network classifiers for distributed privacy-preserving data mining

Neurocomputing

Volumn 150, Issue PB, 2015, Pages 513-528

  Kokkinos, Yiannis ^a   Margaritis, Konstantinos G ^a  

^a UNIVERSITY OF MACEDONIA   (Greece)

Author keywords

Data mining; Distributed computing; Ensemble selection; Neural networks; Privacy preserving

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; DISTRIBUTED COMPUTER SYSTEMS; NEURAL NETWORKS;

AFFINITY PROPAGATION; CLASSIFICATION ACCURACY; ENSEMBLE SELECTIONS; EXPERIMENTAL SIMULATIONS; NEURAL NETWORK CLASSIFIER; PRIVACY PRESERVING; PRIVACY PRESERVING DATA MINING; PRIVACY PRESERVING SOLUTIONS;

DATA PRIVACY;

AFFINITY PROPAGATION ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFIER; CONFIDENCE RATIO; CONTROLLED STUDY; DATA MINING; DISAGREEMENT MEASURE; DISTANCE OF SIGNATURE VECTOR; DISTRIBUTED PRIVACY PRESERVING DATA MINING; DOUBLE FAULT; ENSEMBLE SELECTION; JAM DIVERSITY BASED PRUNING META CLASSIFIER; KAPPA PRUNING; KAPPA STATISTICS; MACHINE LEARNING; RANKING BASED PRUNING; REDUCE ERROR PRUNING; SIMULATION; SUM OF DIFFERENT PREDICTION;

EID: 84922630377     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.07.065     Document Type: Article

References (49)

1. Kargupta H., Sivakumar K. Existential Pleasures of Distributed Data Mining. Data Mining: Next Generation Challenges and Future Directions 2004, AAAI/MIT Press, Cambridge.
2. D. Talia, Distributed data mining tasks and patterns as services, in: Proceedings of the DPA Workshop, 2008, Las Palmas, 2008.
3. Aggarwal C.C., Yu P.S. Privacy-Preserving Data Mining: Models and Algorithms 2008, Kluwer Academic Publishers, Boston.
4. Clifton C., Kantarcioglu M., Vaidya J., Lin X., Zhu M. Tools for privacy-preserving distributed data mining. ACM SIGKDD Explor. 2003, 4(2):1-7.
5. Breiman L. Pasting small votes for classification in large databases and on-line. Mach. Learn. 1999, 36:85-103.
6. Chawla N.V., Hall L.O., Bowyer K.W., Kegelmeyer W.P. Learning ensembles from bites: a scalable and accurate approach. J. Mach Learn. Res. 2004, 5:421-451.
7. Tsoumakas G., Angelis L., Vlahavas I. Clustering classifiers for knowledge discovery from physically distributed databases. Data Knowl. Eng. 2004, 49(3):223-242.
8. Brazdil P., Giraud-Carrier C., Soares C., Vilalta R. Metalearning: Applications to Data Mining 2009, Springer.
9. P. Chan, S. Stolfo, Meta-learning for multistrategy and parallel learning, in: Proceedings of the Second International Workshop on Multistrategy Learning, 1993.
10. A. Prodromidis, S. Stolfo, Pruning meta-classifiers in a distributed data mining system, in: Proceedings of the First National Conference on New Information Technologies, Athens, Greece, 1998, pp. 151-160.
11. Prodromidis A., Chan P., Stolfo S. Meta-learning in distributed data mining systems: Issues and approaches. Advances of Distributed Data Mining 2000, AAAI/MIT Press, Cambridge, MA. H. Kargupta, P. Chan (Eds.).
12. Wang L., Fu X. Data Mining with Computational Intelligence 2005, Springer, Berlin.
13. Seni G., Elder J. Ensemble methods in data mining: improving accuracy through combining predictions 2010, Morgan & Claypool Publishers.
14. Re M., Valentini G. Ensemble methods: a review. Advances in Machine Learning and Data Mining for Astronomy, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series 2012, 563-594. CRC press, Boca Raton.
15. Zhou Z.-H., Wu J., Tang W. Ensembling neural networks: many could be better than all. Artif. Intell. 2002, 137:239-263.
16. Poggio T., Girosi F. Regularization algorithms for learning that are equivalent to multilayer networks. Science 1990, 247:978-982.
17. Girosi F., Jones M., Poggio T. Regularization theory and neural networks architectures. Neural Comput. 1995, 7:219-269.
18. Evgeniou T., Pontil M., Poggio T. Regularization networks and support vector machines. Adv. Comput. Math. 2000, 13(1):1-50.
19. Frey B.J., Dueck D. Clustering by passing messages between data points. Science 2007, 315:972-976.
20. Y. Kokkinos, K. Margaritis, A distributed asynchronous and privacy preserving neural network ensemble selection approach for peer-to-peer data mining, in: ACM Proceedings of the Fifth Balkan Conference in Informatics, 2012, pp. 46-51.
21. M. Kantarcioglu, J. Vaidya, Privacy-preserving naive Bayes classifier for horizontally partitioned data, in: IEEE Workshop on Privacy-Preserving Data Mining, 2003.
22. Yi X., Zhang Y. Privacy-preserving naïve Bayes classification on distributed data via semi-trusted mixers. Inf. Syst. 2009, 34:371-380.
23. H. Yu, X. Jiang, J. Vaidya, Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data, in: Proceedings of the SAC Conference, 2006.
24. L. Xiong, S. Chitti, L. Liu, k nearest neighbour classification across multiple private databases, in: Proceedings of the ACM Fifteenth Conference on Information and Knowledge Management, 5-11 November 2006.
25. D. Margineantu, T. Dietterich, Pruning adaptive boosting, in: Proceedings of the 14th International Conference on Machine Learning, 1997, pp. 211-218.
26. Sharkey A., Sharkey N., Gerecke U., Chandroth G. The test and select approach to ensemble combination. Mult. Classifier Syst. 2000, 30-44.
27. Partalas I., Tsoumakas G., Vlahavas I. Pruning an ensemble of classifiers via reinforcement learning. Neurocomputing 2009, 72:1900-1909.
28. Tsoumakas G., Partalas I., Vlahavas I. An ensemble pruning primer. Applications of Supervised and Unsupervised Ensemble Methods 2009, 245:1-13. Springer, Berlin.
29. C. Tamon, J. Xiang, On the boosting pruning problem, in: Proceedings of 11th European Conference on Machine Learning, Springer, Berlin, 2000, pp. 404-412.
30. Xie Z., Xu Y., Hu Q., Zhu P. Margin distribution based bagging pruning. Neurocomputing 2012, 85:11-19.
31. G. Martínez-Muñoz, A. Suárez, Pruning in ordered bagging ensembles. In Proc. of the 23rd International Conference on Machine Learning, (ICML-2006), pp. 609-616.
32. Martínez-Muñoz G., Hernández-Lobato D., Suárez A. An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(2):245-259.
33. G. Giacinto, F. Roli, G. Fumera, Design of effective multiple classifier systems by clustering of classifiers, in: Proceedings of 15th International Conference on Pattern Recognition, 2000, pp. 160-163.
34. Giacinto G., Roli F. An approach to the automatic design of multiple classifier systems. Pattern Recognit. Lett. 2001, 22:25-33.
35. Liu R., Yuan B. Multiple classifier combination by clustering and selection. Inf. Fusion 2001, 2:163-168.
36. Bakker B., Heskes T. Clustering ensembles of neural network models. Neural Netw. 2003, 16:261-269.
37. A. Lazarevic Z. Obradovic, Effective pruning of neural network classifiers, in: Proceedings of the IEEE/INNS International Conference on Neural Networks, 2001, pp. 796-801.
38. Fu Q., Hu S.-X., Zhao S.-Y. Clustering based selective neural network ensemble. J. Zhejiang Univ. 2005, 6A(5):387-392.
39. Chen H., Yuan S., Jiang K. Selective neural network ensemble based on clustering. Advances in Neural Networks 2006, 3971:545-550. LNCS, Springer, Berlin.
40. H.-L. Yu, G.-F. Chen, D.-Y. Liu, B.-C. Wan, D. Jin, A. Novel Neural network ensemble method based on affinity propagation clustering and lagrange multiplier, in: Proceedings of the International Conference on Computational Intelligence and Software Engineering, 2009.
41. Kuncheva L.I., Whitaker C.J. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 2003, 51:181-207.
42. Tang E.K., Suganthan P.N., Yao X. An analysis of diversity measures. Mach. Learn. 2006, 65:247-271.
43. Kai Li, Hou-Kuan Huang, Xiu-Chen Ye, Li-Juan Cut, A selective approach to neural network ensemble based on clustering technology, in: Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004, pp. 26-29.
44. Yu L., Lai K.K., Wang S. Multistage R.B.F. neural network ensemble learning for exchange rates forecasting. Neurocomputing 2008, 71:3295-3302.
45. Liu Y., Yao X. Simultaneous training of negatively correlated neural networks in an ensemble. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1999, 29(6):716-725.
46. Chen H., Yao X. Regularized negative correlation learning for neural network ensembles. IEEE Trans. Neural Netw. 2009, 20:1962-1979.
47. Wozniak M., Graña M., Corchado E. A survey of multiple classifier systems as hybrid systems. Inf. Fusion 2014, 16:3-17.
48. Lysiak R., Kurzynski M., Woloszynski T. Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers. Neurocomputing 2014, 126:29-35.
49. García S., Fernández A., Luengo J., Herrera F. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 2010, 180:2044-2064.