Embedded Files
A Python library to perform Resampling in
Hierarchical Classification Datasets
The Hierarchical-imblearn
The Hierarchical-imblearn
The full library, as well as its documentation, is hosted in the following GitHub repository:
The full library, as well as its documentation, is hosted in the following GitHub repository:
A Hierarchical Testbed of Datasets
A Hierarchical Testbed of Datasets
Whilest testing the resampling algorithms, we put together a lot of hierarchical datasets with different characteristics and from different application domains. As these datasets are freely available for distribution, we made them all available for download here.
Whilest testing the resampling algorithms, we put together a lot of hierarchical datasets with different characteristics and from different application domains. As these datasets are freely available for distribution, we made them all available for download here.
Datasets Characteristics and References
Datasets Characteristics and References
[1] Klimt, B. and Yang, Y. (2004). Introducing the enron corpus. In Proceedings of the Conference on Email and Anti-Spam (CEAS).
[1] Klimt, B. and Yang, Y. (2004). Introducing the enron corpus. In Proceedings of the Conference on Email and Anti-Spam (CEAS).
[2] Turnbull, D., Barrington, L., Torres, D., and Lanckriet, G. (2007). Towards musical query-by-semantic-description using the cal500 data set. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 439-446. ACM.
[2] Turnbull, D., Barrington, L., Torres, D., and Lanckriet, G. (2007). Towards musical query-by-semantic-description using the cal500 data set. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 439-446. ACM.
[3] Trohidis, K., Tsoumakas, G., Kalliris, G., and Vlahavas, I. P. (2008). Multi-label classification of music into emotions. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), volume 8, pages 325-330.
[3] Trohidis, K., Tsoumakas, G., Kalliris, G., and Vlahavas, I. P. (2008). Multi-label classification of music into emotions. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), volume 8, pages 325-330.
[4] Briggs, F., Huang, Y., Raich, R., Eftaxias, K., Lei, Z., Cukierski, W., Hadley, S. F., Hadley, A., Betts, M., Fern, X. Z., et al. (2013). The 9th annual mlsp competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pages 1-8. IEEE.
[4] Briggs, F., Huang, Y., Raich, R., Eftaxias, K., Lei, Z., Cukierski, W., Hadley, S. F., Hadley, A., Betts, M., Fern, X. Z., et al. (2013). The 9th annual mlsp competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pages 1-8. IEEE.
[5] Parmezan, A. R. S., Souza, V. M., and Batista, G. E. (2018). Towards hierarchical classification of data streams. In Iberoamerican Congress on Pattern Recognition, pages 314-322. Springer.
[5] Parmezan, A. R. S., Souza, V. M., and Batista, G. E. (2018). Towards hierarchical classification of data streams. In Iberoamerican Congress on Pattern Recognition, pages 314-322. Springer.
[6] Metz, J., Freitas, A. A., Monard, M. C., and Cherman, E. A. (2011). A study on the selection of local training sets for hierarchical classification tasks. Encontro Nacional de Inteligncia Artificial, pages 572-583.
[6] Metz, J., Freitas, A. A., Monard, M. C., and Cherman, E. A. (2011). A study on the selection of local training sets for hierarchical classification tasks. Encontro Nacional de Inteligncia Artificial, pages 572-583.
[7] Dimitrovski, I., Kocev, D., Loskovska, S., and Dzeroski, S. (2011). Hierarchical annotation of medical images. Pattern Recognition, 44(10-11):2436-2449.
[7] Dimitrovski, I., Kocev, D., Loskovska, S., and Dzeroski, S. (2011). Hierarchical annotation of medical images. Pattern Recognition, 44(10-11):2436-2449.
[8] Clare, A. and King, R. D. (2003). Predicting gene function in saccharomyces cerevisiae. Bioinformatics, 19(2):42-49.
[8] Clare, A. and King, R. D. (2003). Predicting gene function in saccharomyces cerevisiae. Bioinformatics, 19(2):42-49.
[9] Defferrard, M., Benzi, K., Vandergheynst, P., and Bresson, X. (2017). FMA: A dataset for music analysis. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR).
[9] Defferrard, M., Benzi, K., Vandergheynst, P., and Bresson, X. (2017). FMA: A dataset for music analysis. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR).
[10] Dimitrovski, I., Kocev, D., Loskovska, S., and Dzeroski, S. (2012). Hierarchical classification of diatom images using ensembles of predictive clustering trees. Ecological Informatics, 7(1):19-29.
[10] Dimitrovski, I., Kocev, D., Loskovska, S., and Dzeroski, S. (2012). Hierarchical classification of diatom images using ensembles of predictive clustering trees. Ecological Informatics, 7(1):19-29.
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