Multi-label collective classification using adaptive neighborhoods

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

Volumn 1, Issue , 2012, Pages 427-432

  Saha, Tanwistha ^a   Rangwala, Huzefa ^a   Domeniconi, Carlotta ^a  

^a George Mason University   (United States)

Author keywords

co authorship networks; collective classification; Multi label

Indexed keywords

ADAPTIVE NEIGHBORHOOD; CLASS ASSIGNMENTS; CO-AUTHORSHIP NETWORKS; COLLECTIVE CLASSIFICATIONS; COMPLEX STRUCTURE; CONVERGENCE CRITERION; GRAPH-BASED; LABELED DATA; MOST LIKELY; MULTI-LABEL; MULTI-LABEL CLASSIFICATIONS; MULTI-LABEL LEARNING; RANKING METHODS; REAL-WORLD APPLICATION; REAL-WORLD DATASETS; RELATIONAL DATA;

LEARNING SYSTEMS;

LEARNING ALGORITHMS;

EID: 84873579579     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2012.77     Document Type: Conference Paper

References (17)

1. M. Zhang, "Lift: Multi-label learning with label-specific features,"in Twenty-Second International Joint Conference onArtificial Intelligence, 2011.
2. M. Zhang and Z. Zhou, "Ml-knn: A lazy learning approachto multi-label learning," Pattern Recognition, vol. 40, 2007.
3. M. Zhang and K. Zhang, "Multi-label learning by exploitinglabel dependency," in ACM SIGKDD international conferenceon Knowledge discovery and data mining. ACM, 2010, pp.999-1008.
4. P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, andT. Eliassi-Rad, "Collective classification in network data," AImagazine, vol. 29, no. 3, p. 93, 2008.
5. D. Jensen, J. Neville, and B. Gallagher, "Why collectiveinference improves relational classification," in ACM SIGKDDinternational conference on Knowledge discovery and datamining. ACM, 2004, pp. 593-598.
6. L. Getoor, "Link-based classification," Advanced methods forknowledge discovery from complex data, pp. 189-207, 2005.
7. J. Neville and D. Jensen, "Iterative classification in relationaldata," in Proc. AAAI-2000 Workshop on Learning StatisticalModels from Relational Data, 2000, pp. 13-20.
8. S. Macskassy and F. Provost, "Classification in networkeddata: A toolkit and a univariate case study," The Journal ofMachine Learning Research, vol. 8, pp. 935-983, 2007.
9. L. McDowell, K. Gupta, and D. Aha, "Cautious inferencein collective classification," in National CONFERENCE ONARTIFICIAL INTELLIGENCE, vol. 22, no. 1, 2007, p. 596.
10. X. Kong, X. Shi, and S. Philip, "Multi-label collective classification,"in SIAM International Conference on Data Mining(SDM). SIAM, 2011, pp. 618-629.
11. B. Taskar, P. Abbeel, and D. Koller, "Discriminative probabilisticmodels for relational data," in Eighteenth Conferenceon Uncertainty in Artificial Intelligence (UAI02), 2002.
12. B. Gallagher and T. Eliassi-Rad, "Leveraging labelindependentfeatures for classification in sparsely labelednetworks: An empirical study," Advances in Social NetworkMining and Analysis, pp. 1-19, 2010.
13. G. Tsoumakas and I. Katakis, "Multi-label classification, "International Journal of Data Warehousing & Mining, vol. 3,no. 3, pp. 1-13, 2007.
14. M. Ji, J. Han, and M. Danilevsky, "Ranking-based classificationof heterogeneous information networks," in 17th ACMSIGKDD international conference on Knowledge discoveryand data mining, 2011, pp. 1298-1306.
15. A. Goyal, F. Bonchi, and L. Lakshmanan, "A data-basedapproach to social influence maximization," vol. 5, no. 1.VLDB Endowment, 2011, pp. 73-84.
16. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for supportvector machines," ACM Transactions on Intelligent Systemsand Technology, vol. 2, pp. 27:1-27:27, 2011.
17. Q. Lu and L. Getoor, "Link-based classification," in Proceedingsof the 20th International Conference on MachineLearning (ICML-03), vol. 20, no. 2, 2003, pp. 496-503.