Positive and Unlabeled Multi-Graph Learning

IEEE Transactions on Cybernetics

Volumn 47, Issue 4, 2017, Pages 818-829

  Wu, Jia ^a   Pan, Shirui ^a   Zhu, Xingquan ^b   Zhang, Chengqi ^a   Wu, Xindong ^c  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b Florida Atlantic University   (United States)

^c University of Vermont ^*  (United States)

Author keywords

Classification; features; Graph; multi instance (MI); positive and unlabeled (PU) learning; subgraph

Indexed keywords

CONFIDENCE WEIGHTS; EXPERIMENTAL COMPARISON; FEATURE SPACE; GRAPH CLASSIFICATION; ITERATIVE PROCESS; LEARNING OBJECTIVES; MAXIMUM ACCURACIES; WEIGHT VALUES;

GRAPHIC METHODS;

ARTICLE; CLASSIFIER; LEARNING;

EID: 84961933000     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2016.2527239     Document Type: Article

References (40)

1. J. Cao, Z. Wu, J. Wu, H. Xiong, "SAIL: Summation-based incremental learning for information-theoretic text clustering, " IEEE Trans. Cybern., vol. 43, no. 2, pp. 570-584, Apr. 2013.
2. D. Song, F. Sun, L. Liao, "A hybrid approach for content extraction with text density and visual importance of DOM nodes, " Knowl. Inf. Syst., vol. 42, no. 1, pp. 75-96, 2015.
3. X. Zhu, X. Li, S. Zhang, "Block-row sparse multiview multilabel learning for image classification, " IEEE Trans. Cybern., vol. 46, no. 2, pp. 450-461, Feb. 2016.
4. O. Frunza, D. Inkpen, T. Tran, "A machine learning approach for identifying disease-treatment relations in short texts, " IEEE Trans. Knowl. Data Eng., vol. 23, no. 6, pp. 801-814, Jun. 2011.
5. T. G. Dietterich, R. H. Lathrop, T. L. Lozano-Pérez, "Solving the multiple instance problem with axis-parallel rectangles, " Artif. Intell., vol. 89, nos. 1-2, pp. 31-71, 1997.
6. Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, Y.-F. Li, "Multi-instance multi-label learning, " Artif. Intell., vol. 176, no. 1, pp. 2291-2320, 2012.
7. J. Amores, "MILDE: Multiple instance learning by discriminative embedding, " Knowl. Inf. Syst., vol. 42, no. 2, pp. 381-407, 2015.
8. R. Achanta, et al., "SLIC superpixels compared to state-of-the-art superpixel methods, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
9. S. Pan, J. Wu, X. Zhu, "CogBoost: Boosting for fast cost-sensitive graph classification, " IEEE Trans. Knowl. Data Eng., vol. 27, no. 11, pp. 2933-2946, Nov. 2015.
10. J. Wu, et al., "Multi-graph-view subgraph mining for graph classification, " Knowl. Inf. Syst., pp. 1-26, Sep. 2015, doi: 10. 1007/s10115-015-0872-1.
11. M. Flores-Garrido, J.-A. Carrasco-Ochoa, J. F. Martínez-Trinidad, "AGraP: An algorithm for mining frequent patterns in a single graph using inexact matching, " Knowl. Inf. Syst., vol. 44, no. 2, pp. 385-406, 2015.
12. S. Pan, J. Wu, X. Zhu, C. Zhang, "Graph ensemble boosting for imbalanced noisy graph stream classification, " IEEE Trans. Cybern., vol. 45, no. 5, pp. 954-968, May 2015.
13. X. Yan, H. Cheng, J. Han, P. S. Yu, "Mining significant graph patterns by leap search, " in Proc. SIGMOD, Vancouver, BC, Canada, 2008, pp. 433-444.
14. H. Cheng, X. Yan, J. Han, P. S. Yu, "Direct discriminative pattern mining for effective classification, " in Proc. ICDE, Cancún, Mexico, 2008, pp. 169-178.
15. Y. Zhao, X. Kong, P. S. Yu, "Positive and unlabeled learning for graph classification, " in Proc. ICDM, Vancouver, BC, Canada, 2011, pp. 962-971.
16. X. Li and B. Liu, "Learning to classify texts using positive and unlabeled data, " in Proc. IJCAI, Acapulco, Mexico, 2003, pp. 587-592.
17. B. Liu, Y. Dai, X. Li, W. S. Lee, P. S. Yu, "Building text classifiers using positive and unlabeled examples, " in Proc. ICDM, Melbourne, FL, USA, 2003, pp. 179-188.
18. F. Briggs, X. Z. Fern, R. Raich, "Context-aware MIML instance annotation: Exploiting label correlations with classifier chains, " Knowl. Inf. Syst., vol. 43, no. 1, pp. 53-79, 2015.
19. M. Wang, Y. Gao, K. Lu, Y. Rui, "View-based discriminative probabilistic modeling for 3D object retrieval and recognition, " IEEE Trans. Image Process., vol. 22, no. 4, pp. 1395-1407, Apr. 2013.
20. S. Ray and M. Craven, "Supervised versus multiple instance learning: An empirical comparison, " in Proc. ICML, Bonn, Germany, 2005, pp. 697-704.
21. K. Zhou, X. Gui-Rong, Q. Yang, Y. Yu, "Learning with positive and unlabeled examples using topic-sensitive PLSA, " IEEE Trans. Knowl. Data Eng., vol. 22, no. 1, pp. 46-58, Jan. 2010.
22. B. Liu, W. S. Lee, P. S. Yu, X. Li, "Partially supervised classification of text documents, " in Proc. ICML, Sydney, NSW, Australia, 2002, pp. 387-394.
23. J. T. Zhou, S. J. Pan, Q. Mao, I. W. Tsang, "Multi-view positive and unlabeled learning, " in Proc. ACML, Singapore, 2012, pp. 555-570.
24. N. An, et al., "Toward detection of aliases without string similarity, " Inf. Sci., vol. 261, pp. 89-100, Mar. 2014.
25. K. Riesen and H. Bunke, "Graph classification by means of Lipschitz embedding, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 6, pp. 1472-1483, Dec. 2009.
26. X. Yan and J. Han, "gSpan: Graph-based substructure pattern mining, " in Proc. ICDM, Maebashi City, Japan, 2002, pp. 721-724.
27. M. Wang, et al., "Unified video annotation via multigraph learning, " IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 5, pp. 733-746, May 2009.
28. X. Yang, M. Wang, D. Tao, "Robust visual tracking via multi-graph ranking, " Neurocomputing, vol. 159, pp. 35-43, Jul. 2015.
29. J. Wu, X. Zhu, C. Zhang, Z. Cai, "Multi-instance multi-graph dual embedding learning, " in Proc. ICDM, Dallas, TX, USA, 2013, pp. 827-836.
30. J. Wu, X. Zhu, C. Zhang, P. S. Yu, "Bag constrained structure pattern mining for multi-graph classification, " IEEE Trans. Knowl. Data Eng., vol. 26, no. 10, pp. 2382-2396, Oct. 2014.
31. X. Kong and P. S. Yu, "Semi-supervised feature selection for graph classification, " in Proc. KDD, Washington, DC, USA, 2010, pp. 793-802.
32. Z. Fu, A. Robles-Kelly, J. Zhou, "MILIS: Multiple instance learning with instance selection, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 958-977, May 2011.
33. J. Wu, S. Pan, X. Zhu, Z. Cai, "Boosting for multi-graph classification, " IEEE Trans. Cybern., vol. 45, no. 3, pp. 416-429, Mar. 2015.
34. J. J. McAuley and J. Leskovec, "From amateurs to connoisseurs: Modeling the evolution of user expertise through online reviews, " in Proc. WWW, Rio de Janeiro, Brazil, 2013, pp. 897-908.
35. X. Luo, Z. Xu, J. Yu, X. Chen, "Building association link network for semantic link on Web resources, " IEEE Trans. Autom. Sci. Eng., vol. 8, no. 3, pp. 482-494, Jul. 2011.
36. J. Li and J. Z. Wang, "Real-time computerized annotation of pictures, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 6, pp. 985-1002, Jun. 2008.
37. S. Andrews, I. Tsochantaridis, T. Hofmann, "Support vector machines for multiple-instance learning, " in Proc. NIPS, Whistler, BC, Canada, 2003, pp. 577-584.
38. F. Denis, R. Gilleron, M. Tommasi, "Text classification from positive and unlabeled examples, " in Proc. IPMU, Montpellier, France, 2002, pp. 1927-1934.
39. B. Schölkopf, J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, R. C. Williamson, "Estimating the support of a high-dimensional distribution, " Neural Comput., vol. 13, no. 7, pp. 1443-1471, 2001.
40. K. Wang, N. An, B. N. Li, Y. Zhang, L. Li, "Speech emotion recognition using Fourier parameters, " IEEE Trans. Affect. Comput., vol. 6, no. 1, pp. 69-75, Jan./Mar. 2015.