References
- H.-B. Shen and K.-C. Chou, "A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0," Anaytical Biochemistry, vol. 394, no. 2, pp. 269-274, 2009. https://doi.org/10.1016/j.ab.2009.07.046
- S.-M. Chi and D. Nam, "WegoLoc: accurate prediction of protein subcellular localization using weighted gene ontology terms," Bioinformatics, vol. 28, no. 7, pp. 1028- 1030, 2012. https://doi.org/10.1093/bioinformatics/bts062
- J. He, H. Gu, and W. Liu, "Imbalanced multi-modal multilabel learning for subcellular localization prediction of human proteins with both single and multiple sites," Plos One, vol. 7, no. 6, e37155, 2012. https://doi.org/10.1371/journal.pone.0037155
- S. Mei, "Multi-label multi-kernel transfer learning for human protein subcellular localization," Plos One, vol. 7, no. 6, e37716, 2012. https://doi.org/10.1371/journal.pone.0037716
- G.-Z. Li, X. Wang, X. Hu, J.-M. Liu, and R.-W. Zhao, "Multilabel learning for protein subcellular location prediction," IEEE transactions on Nanobioscience, vol. 11, no. 3, pp. 237-243, 2012. https://doi.org/10.1109/TNB.2012.2212249
- S. Wan, M.-W. Mak, and S.-Y. Kung, "mGOASVM: multilabel protein subcellular localization based on gene ontology and support vector machines," BMC Bioinformatics, 13:290, 2012. https://doi.org/10.1186/1471-2105-13-290
- W.-Z. Lin, J.-A. Fang, X. Xiao, and K.-C. Chou, "iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins," Molecular BioSystems, vol. 9, no. 4, pp. 634-644, 2013. https://doi.org/10.1039/c3mb25466f
- X. Wang and G.-Z. Li, "Multilabel learning via random label selection for protein subcellular multilocations prediction," IEEE transactions on computational biology and bioinformatics, vol. 10, no. 2, pp. 436-446, 2013. https://doi.org/10.1109/TCBB.2013.21
- G. Tsoumakas, I. Katakis, and I. Vlahavas, "Mining multilabel data," in Data Mining and Knowledge Discovery Handbook. Boston, MA: Springer, ch. 34, pp. 667-685, 2010.
- G. Madjarov, D. Kocev, D. Gjorgjevikj, and S. Dzeroski, "An extensive experimental comparison of methods for multi-label learning," Pattern Recognition, vol. 45, no. 9, pp. 3084-3104, 2012. https://doi.org/10.1016/j.patcog.2012.03.004
- M.-L. Zhang and Z-H. Zhou, "A review on multi-label learning algorithms," IEEE transactions on knowledge and data engineering, http://doi.ieeecomputersociety.org/10.1109 /TKDE.2013.39.
- M.-L. Zhang and Z-H. Zhou, "Ml-knn: A lazy learning approach to multi-label learning," Pattern Recognition, vol. 40, no. 7, pp. 2038-2048, 2007. https://doi.org/10.1016/j.patcog.2006.12.019
- E. Spyromitros, G. Tsoumakas, and I. Vlahavas, "An Empirical Study of Lazy Multilabel Classification Algorithms," in Proceeding of the 5th Hellenic Conference on Artificial Intelligence, pp. 401-406, 2008.
- W. Cheng and E. Hullermeier, "Combining instance-based learning and logistic regression for multilabel classification," Machine Learning, vol. 76, no. 2-3, pp. 211-225, 2009. https://doi.org/10.1007/s10994-009-5127-5
- M.-L. Zhang and Z-H. Zhou, "Multi-label neural networks with applications to functional genomics and text categorization," IEEE transactions on knowledge and data engineering, vol. 18, no. 10, pp. 1338-1351, 2006. https://doi.org/10.1109/TKDE.2006.162
- J. Read, B. Pfahringer, H. Geoff, and F. Eibe, "Classifier Chains for Multi-label Classification," Machine Learning, vol. 85, no. 3. pp. 335-359, 2011.
- J. Read, B. Pfahringer, and H. Geoff, "Multi-Label Classification using Ensembles of Pruned Sets," in Proceeding of the 8th IEEE International Conference on Data Mining, pp. 995-1000, 2008.
- J. Furnkranz, E. Hullermeier, E. L. Mencia, and K. Brinker, "Multilabel classification via calibrated label ranking," Machine Learning, vol. 73, no. 2, pp. 133-153, 2008. https://doi.org/10.1007/s10994-008-5064-8
- G. Tsoumakas, I. Katakis, and I. Vlahavas, "Effective and Efficient Multilabel Classification in Domains with Large Number of Labels," in Proceeding of ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD'08), pp. 30-44. 2008.
- G. Nasierding, G. Tsoumakas, and A. Kouzani, "Clustering Based Multi-Label Classification for Image Annotation and Retrieval," in Proceeding of 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4514- 4519, 2009.
- G. Tsoumakas, I. Katakis, and I. Vlahavas, "Random k-Labelsets for Multi-Label Classification," IEEE transactions on knowledge and data engineering, vol. 23, no. 7, pp. 1079- 1089, 2011. https://doi.org/10.1109/TKDE.2010.164
- R. E. Schapire and Y. Singer, "BoosTexter: A boostingbased system for text categorization," Machine learning, vol. 39, no. 2-3, pp. 135-168, 2000. https://doi.org/10.1023/A:1007649029923
- G. Tsoumakas, A. Dimou, E. Spyromitros, V. Mezaris, I. Kompatsiaris, and I. Vlahavas, "Correlation-Based Pruning of Stacked Binary Relevance Models for Multi-Label Learning," in Proceeding of ECML/PKDD 2009 Workshop on Learning from Multi-Label Data (MLD'09), pp. 101- 116, 2009.
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA Data Mining Software: An Update," ACM SIGKDD explorations newsletter, vol. 11, no.1, pp. 10-18, 2009. https://doi.org/10.1145/1656274.1656278
- S.-M. Chi, "Prediction of protein subcellular localization by weighted gene ontology terms," Biochemical and biophysical research communications, vol. 399, no. 3, pp. 402-405, 2010. https://doi.org/10.1016/j.bbrc.2010.07.086
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