A nonparametric classification method based on K-associated graphs

Information Sciences

Volumn 181, Issue 24, 2011, Pages 5435-5456

  Bertini Jr , João Roberto ^a   Zhao, Liang ^a   Motta, Robson ^a   Lopes, Alneu De Andrade ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Graph formation; Graph based learning; K associated graph; Multi class classification; Nonparametric classification; Purity measure

Indexed keywords

GRAPH FORMATION; GRAPH-BASED LEARNING; K-ASSOCIATED GRAPH; MULTI-CLASS CLASSIFICATION; NONPARAMETRIC CLASSIFICATION; PURITY MEASURE;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); INPUT OUTPUT PROGRAMS;

DATA MINING;

EID: 80054694302     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2011.07.043     Document Type: Article

References (55)

1. A. Asuncion, D.J. Newman, UCI machine learning repository, University of California, Irvine, School of Information and Computer Sciences, 2007. [ ].
2. M. Belkin, and P. Niyogi Laplacian eigenmaps for dimensionality reduction and data representation Neural Computation 15 2003 1373 1396 (Pubitemid 37049796)
3. M. Belkin, and P. Niyogi Semi-supervised learning on manifolds Machine Learning 56 2004 209 239
4. M. Belkin, P. Niyogi, and V. Sindhwani Manifold regularization: A geometric framework for learning from labeled and unlabeled examples Journal of Machine Learning Research 1 2006 1 48
5. J. Callut, K. Françoisse, M. Saerens, and P. Dupont Semi-supervised classification from discriminative random walks Lecture Notes on Artificial Inteligence (LNAI) vol. 5211 2008 Springer-Verlag 162 177
6. O. Chapelle, A. Zien, and B. Schölkopf Semi-Supervised Learning first ed. 2006 MIT Press
7. H. Chen, L. Li, and J. Peng Error bounds of multi-graph regularized semi-supervised classification Information Sciences 179 2009 1960 1969
8. J. Chen, H.R. Fang, and Y. Saad Fast approximate knn graph construction for high dimensional data via recursive Lanczos bisection Journal of Machine Learning Research 10 2009 1989 2012
9. T. Cormen, C. Leiserson, R. Rivest, and C. Stein Introduction to Algorithms third ed. 2009 MIT Press
10. M. Culp, and G. Michailidis Graph-based semisupervised learning IEEE Transactions on Pattern Analysis and Machine Intelligence 30 2008 174 179 (Pubitemid 350168364)
11. M. Dehmer, and A. Mowshowitz A history of graph entropy measures Information Sciences 181 2011 51 78
12. O. Delalleau, Y. Bengio, N. Roux, Efficient non-parametric function induction in semi-supervised learning, in: Proceedings of the 10th International Workshop on Artificial Intelligence and Statitics, Society for Artificial Intelligence and Statistics, 2005, pp. 96-103.
13. J. Dems̀ar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
14. R. Duda, P. Hart, and D. Stork Pattern Classification 2nd ed. 2001 John Wiley & Sons, Inc
15. D. Gómez, J. Montero, and J. Yánez A coloring fuzzy graph approach for image classification Information Sciences 176 2006 3645 3657 (Pubitemid 44415824)
16. J. Han, and M. Kamber Data Mining: Concepts and Techniques second ed. 2006 Morgan Kaufman San Francisco, U.S.
17. T. Hastie, R. Tibshirani, and J. Friedman The Elements of Statistical Learning: Data Mining, Inference and Prediction second ed. 2009 Springer-Verlag Canada
18. M. Hein, J.Y. Audibert, and U. von Luxburg Graph Laplacians and their convergence on random neighborhood graphs Journal of Machine Learning Research 8 2007 1325 1368
19. S. Hernandez-Rodriguez, J. Martinez-Trinidad, and J. Carrasco-Ochoa Fast k most similar neighbor classifier for mixed data (tree k-msn) Pattern Recognition 43 2010 873 886
20. V. Hodge, and J. Austin A survey of outlier detection methodologies Artificial Intelligence Review 22 2004 85 126
21. C.W. Hsu, and C.J. Lin A comparison of methods for multiclass support vector machines IEEE Transactions on Neural Networks 13 2002 415 425
22. Q. Hu, D. Yu, J. Liu, and C. Wu Neighborhood rough set based heterogeneous feature subset selection Information Sciences 178 2008 3577 3594
23. Q. Hu, D. Yu, and Z. Xie Neighborhood classifiers Expert Systems with Applications 34 2008 866 876 (Pubitemid 350117461)
24. D. Jensen, J. Neville, and B. Gallagher Why collective inference improves relational classification Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD04) 2004 ACM 593 598 (Pubitemid 40114976)
25. T. Kajdanowicz, P. Kazienko, and P. Doskocz Label-dependent feature extraction in social networks for node classification Proceedings of the Second International Conference on Social Informatics (SocInfo10) Lecture Notes in Computer Science (LNCS) 2010 Springer 89 102
26. J.H. Kim Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap Computational Statistics and Data Analysis 53 2009 3735 3745
27. B. Krishnapuram, D. Williams, Y. Xue, A. Hartemink, L. Carin, and M. Figueiredo On semi-supervised classification L.K. Saul, Y. Weiss, L. Bottou, Advances in Neural Information Processing Systems vol. 17 2005 MIT Press Cambridge, MA 721 728
28. C.H. Lee, O. Zainȯane, H.H. Park, J. Huang, and R. Creiner Clustering high dimensional data: a graph-based relaxed optimization approach Information Sciences 178 2008 4501 4511
29. A.A. Lopes, J.R. Bertini Jr., R. Motta, and L. Zhao Classification based on the optimal k-associated network Proceedings of the 1st International Conference on Complex Sciences: Theory and Applications (COMPLEX09) Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST) vol. 4 2009 Springer 1167 1177
30. Q. Lu, and L. Getoor Link-based classification Proceedings of the 20th International Conference on Machine Learning (ICML03) 2003 ACM Press 496 503
31. U. von Luxburg A tutorial on spectral clustering Statistical Computation 17 2007 395 416
32. S. Macskassy, and F. Provost A simple relational classifier Proceedings of the 2nd Multi-Relational Data Mining Workshop, 9th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD03) 2003 ACM 64 76
33. S. Macskassy, and F. Provost Classification in networked data: a toolkit and a univariate case study Journal of Machine Learning Research 8 2007 935 983 (Pubitemid 46798412)
34. S. Mathew, and M.S. Sunitha Types of arcs in a fuzzy graph Information Sciences 179 2009 1760 1768
35. L. McDowell, K. Gupta, and D. Aha Cautious collective classification Journal of Machine Learning Research 10 2009 2777 2836
36. T. Mitchell Machine Learning, McGraw-Hill Series in Computer Science 1st ed. 1997 McGraw-Hill
37. Y. Mizoguchi Properties of graphs preserved by relational graph rewritings Information Sciences 119 1999 289 299 (Pubitemid 30530425)
38. J. Neville, D. Jensen, L. Friedland, and M. Hay Learning relational probability trees Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD03) 2003 ACM 625 630
39. J.R. Quinlan C4.5: Programs for Machine Learning 1st ed. 1993 Morgan Kaufman Publishers
40. S. Schaeffer Graph clustering Computer Science Review 1 2007 27 34
41. E. Segal, H. Wang, and D. Koller Discovering molecular pathways from protein interaction and gene expression data Bioinformatics 19 2003 264 276
42. P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad Collective classification in network data Artificial Intelligence Magazine 29 2008 93 106
43. V. Sindhwani, P. Niyogi, and M. Belkin Beyond the point cloud: from transductive to semi-supervised learning Proceedings of the 22nd International Conference on Machine Learning (ICML05) 2005 ACM Press 824 831
44. M. Szummer, and T. Jaakkola Partially labeled classification with markov random walks T. Dietterich, S. Becker, Z. Ghahramani, Advances in Neural Information Processing Systems vol. 14 2002 MIT Press Cambridge, MA 945 952
45. B. Taskar, P. Abbeel, D. Koller, Discriminative probalistic models for relational data, in: Proceedings of the 18th Conference on Uncertainity in Artificial Intelligence (UAI02), AUAI Press, 2002, pp. 485-492.
46. V. Vapnik The Nature of Statistical Learning Theory second ed. 1999 Springer-Verlag
47. A. Vathy-Fogarassy, and J. Abonyi Local and global mappings of topology representing networks Information Sciences 179 2009 3791 3803
48. S.V.N. Vishwanathan, N. Schraudolph, R. Kondor, and K.M. Borgwardt Graph kernels Journal of Machine Learning Research 11 2010 1201 1242
49. F. Wang, and C. Zhang Label propagation through linear neighborhoods IEEE Transactions On Knowledge and Data Engineering 20 2008 55 67
50. J. Wang, F. Wang, C. Zhang, H.C. Shen, and L. Quan Linear neighborhood propagation and its applications IEEE Transactions on Pattern Analysis and Machine Intelligence 31 2009 1600 1615
51. D. Watts, and S. Strogatz Collective dynamics of small-world networks Nature 393 1998 440 442
52. S. Yue, J.S. Wang, T. Wu, and H. Wang A new separation measure for improving the effectiveness of validity indices Information Sciences 180 2010 748 764
53. X. Zhu, Semi-Supervised Learning Literature Survey, Technical Report 1530, Computer-Science, University of Wisconsin-Madison, 2008.
54. X. Zhu, and J. Lafferty Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning Proceedings of the 22nd International Conference on Machine Learning (ICML05) 2005 ACM Press 1052 1059
55. X. Zhu, J. Lafferty, Z. Ghahramani, Semi-Supervised Learning: From Gaussian Fields to Gaussian Processes, Technical Report CMU-CS-03-175, Carnegie Mellon University, 2003.