Mandatory leaf node prediction in hierarchical multilabel classification

IEEE Transactions on Neural Networks and Learning Systems

Volumn 25, Issue 12, 2014, Pages 2275-2287

  Bi, Wei ^a   Kwok, James T ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Bayesian decision; hierarchical classification; integer linear program (ILP); multilabel classification.

Indexed keywords

DIRECTED GRAPHS; DYNAMIC PROGRAMMING; FORECASTING; FORESTRY; INTEGER PROGRAMMING; SEMANTICS; TREES (MATHEMATICS);

BAYESIAN DECISION; DIRECTED ACYCLIC GRAPH (DAG); HIERARCHICAL CLASSIFICATION; HIERARCHICAL MULTI-LABEL CLASSIFICATIONS; INTEGER LINEAR PROGRAMS; MULTI-CLASS CLASSIFICATION; MULTI-LABEL CLASSIFICATIONS; POSTERIOR PROBABILITY;

CLASSIFICATION (OF INFORMATION);

CLASSIFICATION; LABELS;

EID: 84913528724     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2014.2309437     Document Type: Article

References (44)

1. C. Vens, J. Struyf, L. Schietgat, S. Dvzeroski, and H. Blockeel, "Decision trees for hierarchical multi-label classification," Mach. Learn., vol. 73, no. 2, pp. 185-214, 2008.
2. J. Burred and A. Lerch, "A hierarchical approach to automatic musical genre classification," in Proc. 6th Int. Conf. Digit. Audio Effects, London, U.K., Sep. 2003.
3. J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor, "Kernelbased learning of hierarchical multilabel classification models," J. Mach. Learn. Res., vol. 7, pp. 1601-1626, Dec. 2006.
4. W. Bi and J.-T. Kwok, "Multi-label classification on tree-and DAGstructured hierarchies," in Proc. 28th Int. Conf. Mach. Learn., Bellevue, WA, USA, 2011, pp. 17-24.
5. N. Cesa-Bianchi, C. Gentile, and L. Zaniboni, "Incremental algorithms for hierarchical classification," J. Mach. Learn. Res., vol. 7, pp. 31-54, Dec. 2006.
6. N. Cesa-Bianchi, C. Gentile, and L. Zaniboni, "Hierarchical classification: Combining Bayes with SVM," in Proc. 23rd Int. Conf. Mach. Learn., Pittsburgh, PA, USA, 2006, pp. 177-184.
7. Z. Barutcuoglu and O. Troyanskaya, "Hierarchical multi-label prediction of gene function," Bioinformatics, vol. 22, no. 7, pp. 830-836, 2006.
8. C. Silla and A. Freitas, "A survey of hierarchical classification across different application domains," Data Mining Knowl. Discovery, vol. 22, no. 1, pp. 31-72, 2011.
9. K. Punera, S. Rajan, and J. Ghosh, "Automatically learning document taxonomies for hierarchical classification," in Proc. 14th Int. Conf. World Wide Web, Chiba, Japan, 2005, pp. 1010-1011.
10. M.-L. Zhang and K. Zhang, "Multi-label learning by exploiting label dependency," in Proc. 16th Int. Conf. Knowl. Discovery Data Mining, Washington, DC, USA, 2010, pp. 999-1008.
11. S. Bengio, J. Weston, and D. Grangier, "Label embedding trees for large multi-class tasks," in Proc. Adv. NIPS, vol. 23. 2010, pp. 163-171.
12. J. Deng, S. Satheesh, A. Berg, and L. Fei-Fei, "Fast and balanced: Efficient label tree learning for large scale object recognition," in Proc. Adv. NIPS, vol. 24. 2011, pp. 567-575.
13. R. Cerri, A. C. P. L. F. De Carvalho, and A. A. Freitas, "Adapting nonhierarchical multilabel classification methods for hierarchical multilabel classification," Intell. Data Anal., vol. 15, no. 6, pp. 861-887, 2011.
14. G. Tsoumakas and I. Vlahavas, "Random k-labelsets: An ensemble method for multilabel classification," in Proc. 18th Eur. Conf. Mach. Learn., Warsaw, Poland, 2007, pp. 406-417.
15. D. Koller and M. Sahami, "Hierarchically classifying documents using very few words," in Proc. 14th Int. Conf. Mach. Learn., Nashville, TN, USA, 1997, pp. 170-178.
16. D. Zhou, L. Xiao, and M. Wu, "Hierarchical classification via orthogonal transfer," in Proc. 28th Int. Conf. Mach. Learn., Bellevue, WA, USA, 2011, pp. 801-808.
17. W.-L. Zhong, W. Pan, J.-Y. Kwok, and I.-H. Tsang, "Incorporating the loss function into discriminative clustering of structured outputs," IEEE Trans. Neural Netw., vol. 21, no. 10, pp. 1564-1575, Oct. 2010.
18. K. Dembczynski, W. Cheng, and E. Hüllermeier, "Bayes optimal multilabel classification via probabilistic classifier chains," in Proc. 27th Int. Conf. Mach. Learn., Haifa, Israel, 2010, pp. 279-286.
19. L. Cai and T. Hofmann, "Exploiting known taxonomies in learning overlapping concepts," in Proc. 20th Int. Joint Conf. Artif. Intell., Pasadena, CA, USA, 2007, pp. 714-719.
20. W. Bi and J.-T. Kwok, "Mandatory leaf node prediction in hierarchical multi-label classification," in Proc. Adv. NIPS, vol. 25. 2012, pp. 153-161.
21. G. Tsoumakas and I. Katakis, "Multi-label classification: An overview," Int. J. Data Warehousing Mining, vol. 3, no. 3, pp. 1-13, 2007.
22. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables," J. Mach. Learn. Res., vol. 6, no. 2, p. 1453, 2005.
23. G. Tsoumakas, I. Katakis, and I. Vlahavas, "Mining multi-label data," in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds., 2nd ed. New York, NY, USA: Springer-Verlag, 2010, pp. 667-685.
24. J. Barbedo and A. Lopes, "Automatic genre classification of musical signals," EURASIP J. Appl. Signal Process., vol. 2007, no. 1, p. 157, 2007.
25. L. Tang, S. Rajan, and V. Narayanan, "Large scale multi-label classification via metalabeler," in Proc. 18th Int. Conf. World Wide Web, Madrid, Spain, 2009, pp. 211-220.
26. J. Zaragoza, L. Sucar, and E. Morales, "Bayesian chain classifiers for multidimensional classification," in Proc. 22nd Int. Joint Conf. Artif. Intell., Barcelona, Spain, 2011, pp. 2192-2197.
27. J. C. Platt, "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods," in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Eds. Cambridge, MA, USA: MIT Press, 1999, pp. 61-74.
28. G. Valentini, "True path rule hierarchical ensembles for genomewide gene function prediction," IEEE/ACM Trans. Comput. Biol. Bioinformat., vol. 8, no. 3, pp. 832-847, Jun. 2011.
29. S. Shimony, "Finding MAPs for belief networks is NP-hard," Artif. Intell., vol. 68, no. 2, pp. 399-410, 1994.
30. C. Varin, N. Reid, and D. Firth, "An overview of composite likelihood methods," Statist. Sinica, vol. 21, no. 1, pp. 5-42, 2011.
31. Y. Zhang and J. Schneider, "A composite likelihood view for multilabel classification," in Proc. 15th Int. Conf. Artif. Intell. Statist., Ft. Lauderdale, FL, USA, 2012, pp. 1407-1415.
32. R. Baraniuk, V. Cevher, M. Duarte, and C. Hegde, "Model-based compressive sensing," IEEE Trans. Inf. Theory, vol. 56, no. 4, pp. 1982-2001, Apr. 2010.
33. D. Lewis, Y. Yang, T. Rose, and F. Li, "RCV1: A new benchmark collection for text categorization research," J. Mach. Learn. Res., vol. 5, pp. 361-397, Dec. 2004.
34. G. Tsoumakas, I. Katakis, and I. Vlahavas, "Effective and efficient multilabel classification in domains with large number of labels," in Proc. ECML/PKDD Workshop MMD, Antwerp, Belgium, 2008, pp. 30-44.
35. P. Heymann and H. Garcia-Molina, "Collaborative creation of communal hierarchical taxonomies in social tagging systems," Stanford InfoLab, Stanford, CA, USA, Tech. Rep. 2006-10, Apr. 2006.
36. B. Klimt and Y. Yang, "The Enron corpus: A new dataset for email classification research," in Proc. 18th Eur. Conf. Mach. Learn., Pisa, Italy, 2004, pp. 217-226.
37. L. Fei-Fei, R. Fergus, and P. Perona, "One-shot learning of object categories," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 4, pp. 594-611, Apr. 2006.
38. G. Griffin, A. Holub, and P. Perona, "Caltech-256 object category dataset," California Inst. Technol., Pasadena, CA, USA, Tech. Rep. CNS-TR-2007-001, 2007.
39. J. Read, B. Pfahringer, G. Holmes, and E. Frank, "Classifier chains for multi-label classification," in Proc. Eur. Conf. Mach. Learn., 2009, pp. 254-269.
40. J. Petterson and T. Caetano, "Submodular multi-label learning," in Proc. Adv. NIPS, 2011, pp. 1512-1520.
41. J. Zhou, J. Chen, and J. Ye, MALSAR: Multi-tAsk Learning via StructurAl Regularization. Phoenix, AZ, USA: Arizona State Univ., 2011.
42. G. Cesa-Bianchi and N. Valentini, "Hierarchical cost-sensitive algorithms for genome-wide gene function prediction," J. Mach. Learn. Res., vol. 8, pp. 14-29, Mar. 2010.
43. S. Garca and F. Herrera, "An extension on 'statistical comparisons of classifiers over multiple data sets' for all pairwise comparisons," J. Mach. Learn. Res., vol. 9, no. 12, pp. 2677-2694, 2008.
44. J. Demvsar, "Statistical comparisons of classifiers over multiple data sets," J. Mach. Learn. Res., vol. 7, pp. 1-30, Jan. 2006.