Ensemble approaches for large-scale multi-label classification and question answering in biomedicine

CEUR Workshop Proceedings

Volumn 1180, Issue , 2014, Pages 1348-1360

  Papanikolaou, Yannis ^a   Dimitriadis, Dimitrios ^a   Tsoumakas, Grigorios ^a   Laliotis, Manos ^b   Markantonatos, Nikos ^c   Vlahavas, Ioannis ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b Atypon   (United States)

^c Atypon Hellas   (Greece)

Author keywords

BioASQ; Ensemble methods; Latent Dirichlet Allocation; Multi label learning; Support vector machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); SEMANTICS; STATISTICS; SUPPORT VECTOR MACHINES;

BIOASQ; ENSEMBLE APPROACHES; ENSEMBLE METHODS; LATENT DIRICHLET ALLOCATION; MULTI LABEL CLASSIFICATION; MULTI-LABEL LEARNING; QUESTION ANSWERING; QUESTION ANSWERING TASK;

INDEXING (OF INFORMATION);

EID: 84980637890     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Weissenborn, D., Tsatsaronis, G., Schroeder, M.: Answering factoid questions in the biomedical domain. In Ngomo, A. C. N., Paliouras, G., eds.: BioASQ@CLEF. Volume 1094 of CEUR Workshop Proceedings., CEUR-WS.org (2013)
2. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20 (3) (1995) 273-297
3. Fan, R. E., Chang, K. W., Hsieh, C. J., Wang, X. R., Lin, C. J.: Liblinear: A library for large linear classification. J. Mach. Learn. Res. 9 (June 2008) 1871-1874
4. Tang, L., Rajan, S., Narayanan, V. K.: Large scale multi-label classification via metalabeler. In: WWW'09: Proceedings of the 18th international conference on World wide web, New York, NY, USA, ACM (2009) 211-220
5. Yang, Y.: A study of thresholding strategies for text categorization. In: SIGIR'01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA, ACM (2001) 137-145
6. Fan, R. E., Lin, C. J.: A study on threshold selection for multi-label classification. Technical report, National Taiwan University (2007)
7. Nam, J., Kim, J., Gurevych, I., Fürnkranz, J.: Large-scale multi-label text classification - revisiting neural networks. CoRR abs/1312.5419 (2013)
8. Blei, D. M., Ng, A. Y., Jordan, M. I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3 (March 2003) 993-1022
9. Griffths, T. L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences 101 (Suppl. 1) (April 2004) 5228-5235
10. Ramage, D., Hall, D., Nallapati, R., Manning, C. D.: Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1. EMNLP'09, Stroudsburg, PA, USA, Association for Computational Linguistics (2009) 248-256
11. Rubin, T. N., Chambers, A., Smyth, P., Steyvers, M.: Statistical topic models for multi-label document classification. Mach. Learn. 88 (1-2) (July 2012) 157-208
12. Yang, Y., Zhang, J., Kisiel, B.: A scalability analysis of classifiers in text categorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. SIGIR'03, New York, NY, USA, ACM (2003) 96-103
13. Dietterich, T. G.: Ensemble Methods in Machine Learning. In: Proceedings of the 1st International Workshop in Multiple Classifier Systems. (2000) 1-15
14. Wolpert, D. H.: Original contribution: Stacked generalization. Neural Netw. 5 (2) (February 1992) 241-259
15. Breiman, L.: Bagging predictors. Mach. Learn. 24 (2) (August 1996) 123-140
16. Schapire, R. E.: The strength of weak learnability. Mach. Learn. 5 (2) (July 1990) 197-227
17. Tahir, M. A., Kittler, J., Bouridane, A.: Multilabel classification using heterogeneous ensemble of multi-label classifiers. Pattern Recogn. Lett. 33 (5) (2012) 513-523
18. Jimeno-Yepes, A., Mork, J. G., Demner-Fushman, D., Aronson, A. R.: A onesizefits-all indexing method does not exist: Automatic selection based on metalearning. JCSE 6 (2) (2012) 151-160
19. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
20. Forman, G.: BNS feature scaling: an improved representation over tf-idf for svm text classification. In: Proceedings of the 17th ACM conference on Information and knowledge management. CIKM'08, New York, NY, USA, ACM (2008) 263-270
21. Lewis, D. D., Yang, Y., Rose, T. G., Li, F.: Rcv1: A new benchmark collection for text categorization research. J. Mach. Learn. Res. 5 (2004) 361-397
22. Aronson, A. R., Lang, F. M.: An overview of metamap: historical perspective and recent advances. JAMIA 17 (3) (2010) 229-236