Exploiting label dependency for hierarchical multi-label classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7301 LNAI, Issue PART 1, 2012, Pages 294-305

  Alaydie, Noor ^a   Reddy, Chandan K ^a   Fotouhi, Farshad ^a  

^a Wayne State University   (United States)

Author keywords

boosting; correlation; Hierarchical multi label classification

Indexed keywords

BAYES METHOD; BIO-MOLECULAR; BOOSTING; CLASS HIERARCHIES; CLASSIFICATION ALGORITHM; DATA SETS; HIERARCHICAL TAXONOMY; MULTI-LABEL; NOVEL ALGORITHM; ONTOLOGICAL STRUCTURES; TRAINING SETS;

ALGORITHMS; OPTICAL CORRELATION; TAXONOMIES;

DATA MINING;

EID: 84861432903     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-30217-6_25     Document Type: Conference Paper

References (24)

1. Alaydie, N., Reddy, C.K., Fotouhi, F.: Hierarchical boosting for gene function prediction. In: Proceedings of the 9th International Conference on Computational Systems Bioinformatics (CSB), Stanford, CA, USA, pp. 14-25 (August 2010)
2. Alaydie, N., Reddy, C.K., Fotouhi, F.: A Bayesian Integration Model of Heterogeneous Data Sources for Improved Gene Functional Inference. In: Proceedings of the ACMConference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB), Chicago, IL, USA, pp. 376-380 (August 2011)
3. Barutcuoglu, Z., Schapire, R.E., Troyanskaya, O.G.: Hierarchical multi-label prediction of gene function. Bioinformatics 22(7), 830-836 (2006)
4. Bi, W., Kwok, J.: Multi-Label Classification on Tree- and DAG-Structured Hierarchies. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 17-24. ACM, New York (2011)
5. Cesa-Bianchi, N., Valentini, G.:Hierarchical cost-sensitive algorithms for genome-wide gene function prediction. In: Proceedings of the Third International Workshop on Machine Learning in Systems Biology, Ljubljana, Slovenia, pp. 25-34 (2009)
6. Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Machine Learning 76(2-3), 211-225 (2009)
7. The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics 25(1), 25-29 (2000)
8. Deng, M., Chen, T., Sun, F.: An integrated probabilistic model for functional prediction of proteins. In: Proc. 7th Int. Conf. Comp. Mol. Biol., pp. 95-103 (2003)
9. Esuli, A., Fagni, T., Sebastiani, F.: Boosting multi-label hierarchical text categorization. Information Retrieval 11, 287-313 (2008)
10. Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., Botstein, D., Brown, P.O.: Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241-4257 (2000)
11. Jun, G., Ghosh, J.: Multi-class Boosting with Class Hierarchies. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 32-41. Springer, Heidelberg (2009)
12. Mostafavi, S.,Morris, Q.: Using the gene ontology hierarchy when predicting gene function. In: Conference on Uncertainty in Artificial Intelligence (UAI), Montreal, Canada, pp. 22-26 (September 2009)
13. Palit, I., Reddy, C.K.: Scalable and Parallel Boosting with MapReduce. IEEE Transactions on Knowledge and Data Engineering, TKDE (in press, 2012)
14. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier Chains for Multi-label Classification. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 254-269. Springer, Heidelberg (2009)
15. Reddy, C.K., Park, J.-H.: Multi-resolution Boosting for Classification and Regression Problems. Knowledge and Information Systems (KAIS) 29(2), 435-456 (2011)
16. Rousu, J., Saunders, C., Szedmak, S., Shawe-Taylor, J.: Kernel-Based Learning of Hierarchical Multilabel Classification Models. The Journal of Machine Learning Research 7, 1601-1626 (2006)
17. Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Güldener, U., Mannhaupt, G., Münsterkötter, M., Mewes, H.W.: The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research 32(18), 5539-5545 (2004)
18. Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery 22, 31-72 (2011)
19. Stark, C., Breitkreutz, B., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Research 34, D535-D539 (2006)
20. Valentini, G.: True path rule hierarchical ensembles for genome-wide gene function prediction. IEEE ACM Transactions on Computational Biology and Bioinformatics 8(3), 832-847 (2011)
21. Vens, C., Struyf, J., Schietgat, L., Dz̃eroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Machine Learning 73, 185-214 (2008)
22. Von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S., Fields, S., Bork, P.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399-403 (2002)
23. Yan, R., Tesic, J., Smith, J.R.: Model-Shared Subspace Boosting for Multi-label Classification. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), New York, NY, USA, pp. 834-843 (2007)
24. Zhang, M.-L., Zhang, K.: Multi-label learning by exploiting label dependency. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, D.C., USA, pp. 999-1007 (2010)