How to cite this paper
Ado, A., Bichi, A., Haruna, U., Almaiah, M., Shawai, Y., AlAli, R., Alkhdour, T., Aldhyani, T., Al-rawad, M & Shehab, R. (2024). An improved multi-stage framework for large-scale hierarchical text classification problems using a modified feature hashing and bi-filtering strategy.International Journal of Data and Network Science, 8(4), 2193-2204.
Refrences
Ado, A., Deris, M. M., Samsudin, N. A., & Aliyu, A. (2021). A new feature filtering approach by integrating IG and T-test evaluation metrics for text classification. International Journal of Advanced Computer Science and Applications, 12(6).
Ado, A., Samsudin, N. A., & Deris, M. M. (2021, July). A new feature hashing approach based on term weight for dimension-al reduction. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1-7). IEEE.
Azeez, N. A., Lawal, A. O., Misra, S., & Oluranti, J. (2022). Machine learning approach for identifying suspicious uniform re-source locators (URLs) on Reddit social network. African Journal of Science, Technology, Innovation and Development, 14(6), 1618-1626.
Babbar, R., Partalas, I., Gaussier, E., Amini, M. R., & Amblard, C. (2016). Learning taxonomy adaptation in large-scale clas-sification. Journal of Machine Learning Research, 17(98), 1-37.
Charuvaka, A., & Rangwala, H. (2015). Hiercost: Improving large scale hierarchical classification with cost sensitive learning. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I 15 (pp. 675-690). Springer International Publishing.
Cunningham, J. P., & Ghahramani, Z. (2015). Linear dimensionality reduction: Survey, insights, and generalizations. The Journal of Machine Learning Research, 16(1), 2859-2900.
Dhillon, I. S., Mallela, S., & Kumar, R. (2003). A divisive information theoretic feature clustering algorithm for text classifica-tion. The Journal of machine learning research, 3, 1265-1287.
Fall, C. J., Törcsvári, A., Benzineb, K., & Karetka, G. (2003, April). Automated categorization in the international patent clas-sification. In Acm Sigir Forum (Vol. 37, No. 1, pp. 10-25). New York, NY, USA: ACM.
Gopal, S., & Yang, Y. (2013, August). Recursive regularization for large-scale classification with hierarchical and graphical dependencies. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data min-ing (pp. 257-265).
Guo, Y., Liu, Y., Bakker, E. M., Guo, Y., & Lew, M. S. (2018). CNN-RNN: a large-scale hierarchical image classification framework. Multimedia tools and applications, 77(8), 10251-10271.
Krishnan, R., Samaranayake, V. A., & Jagannathan, S. (2019). A hierarchical dimension reduction approach for big data with application to fault diagnostics. Big Data Research, 18, 100121.
Mladenić, D., & Grobelnik, M. (2003). Feature selection on hierarchy of web documents. Decision support systems, 35(1), 45-87.
Naik, A., & Rangwala, H. (2016a). Filter based taxonomy modification for improving hierarchical classification. arXiv preprint arXiv:1603.00772.
Naik, A., & Rangwala, H. (2016b). Embedding feature selection for large-scale hierarchical classification. In 2016 IEEE Inter-national Conference on Big Data (Big Data) (pp. 1212-1221). IEEE.
Naik, A., & Rangwala, H. (2018). Large scale hierarchical classification: state of the art. Springer International Publishing.
Partalas, I., Kosmopoulos, A., Baskiotis, N., Artieres, T., Paliouras, G., Gaussier, E., ... & Galinari, P. (2015). Lshtc: A benchmark for large-scale text classification. arXiv preprint arXiv:1503.08581.
Pavia, S., Piraino, N., Islam, K., Pyayt, A., & Gubanov, M. N. (2022). Hybrid Metadata Classification in Large-scale Struc-tured Datasets. J. Data Intell., 3(4), 460-473.
Pilnenskiy, N., & Smetannikov, I. (2020). Feature selection algorithms as one of the python data analytical tools. Future Inter-net, 12(3), 54.
Ramírez-Corona, M., Sucar, L. E., & Morales, E. F. (2016). Hierarchical multilabel classification based on path evalua-tion. International Journal of Approximate Reasoning, 68, 179-193.
Ristoski, P., & Paulheim, H. (2014). Feature selection in hierarchical feature spaces. In Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings 17 (pp. 288-300). Springer International Publish-ing.
Rong, M., Gong, D., & Gao, X. (2019). Feature selection and its use in big data: challenges, methods, and trends. Ieee Access, 7, 19709-19725.
Roul, R. K., & Sahoo, J. K. (2018). Text categorization using a novel feature selection technique combined with ELM. In Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 3 (pp. 217-228). Springer Singapore.
Silla, C. N., & Freitas, A. A. (2011). A survey of hierarchical classification across different application domains. Data mining and knowledge discovery, 22, 31-72.
Stein, R. A., Jaques, P. A., & Valiati, J. F. (2019). An analysis of hierarchical text classification using word embed-dings. Information Sciences, 471, 216-232.
Vora, S., & Yang, H. (2017, July). A comprehensive study of eleven feature selection algorithms and their impact on text clas-sification. In 2017 Computing Conference (pp. 440-449). IEEE.
Weinberger, K., Dasgupta, A., Langford, J., Smola, A., & Attenberg, J. (2009, June). Feature hashing for large scale multitask learning. In Proceedings of the 26th annual international conference on machine learning (pp. 1113-1120).
Wibowo, W., & Williams, H. E. (2002, November). Strategies for minimising errors in hierarchical web categorisation. In Pro-ceedings of the eleventh international conference on Information and knowledge management (pp. 525-531).
Zhao, F., Huang, Y., Wang, L., & Tan, T. (2015). Deep semantic ranking based hashing for multi-label image retrieval. In Pro-ceedings of the IEEE conference on computer vision and pattern recognition (pp. 1556-1564).
Zhou, D., Xiao, L., & Wu, M. (2011). Hierarchical classification via orthogonal transfer.
Ado, A., Samsudin, N. A., & Deris, M. M. (2021, July). A new feature hashing approach based on term weight for dimension-al reduction. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1-7). IEEE.
Azeez, N. A., Lawal, A. O., Misra, S., & Oluranti, J. (2022). Machine learning approach for identifying suspicious uniform re-source locators (URLs) on Reddit social network. African Journal of Science, Technology, Innovation and Development, 14(6), 1618-1626.
Babbar, R., Partalas, I., Gaussier, E., Amini, M. R., & Amblard, C. (2016). Learning taxonomy adaptation in large-scale clas-sification. Journal of Machine Learning Research, 17(98), 1-37.
Charuvaka, A., & Rangwala, H. (2015). Hiercost: Improving large scale hierarchical classification with cost sensitive learning. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I 15 (pp. 675-690). Springer International Publishing.
Cunningham, J. P., & Ghahramani, Z. (2015). Linear dimensionality reduction: Survey, insights, and generalizations. The Journal of Machine Learning Research, 16(1), 2859-2900.
Dhillon, I. S., Mallela, S., & Kumar, R. (2003). A divisive information theoretic feature clustering algorithm for text classifica-tion. The Journal of machine learning research, 3, 1265-1287.
Fall, C. J., Törcsvári, A., Benzineb, K., & Karetka, G. (2003, April). Automated categorization in the international patent clas-sification. In Acm Sigir Forum (Vol. 37, No. 1, pp. 10-25). New York, NY, USA: ACM.
Gopal, S., & Yang, Y. (2013, August). Recursive regularization for large-scale classification with hierarchical and graphical dependencies. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data min-ing (pp. 257-265).
Guo, Y., Liu, Y., Bakker, E. M., Guo, Y., & Lew, M. S. (2018). CNN-RNN: a large-scale hierarchical image classification framework. Multimedia tools and applications, 77(8), 10251-10271.
Krishnan, R., Samaranayake, V. A., & Jagannathan, S. (2019). A hierarchical dimension reduction approach for big data with application to fault diagnostics. Big Data Research, 18, 100121.
Mladenić, D., & Grobelnik, M. (2003). Feature selection on hierarchy of web documents. Decision support systems, 35(1), 45-87.
Naik, A., & Rangwala, H. (2016a). Filter based taxonomy modification for improving hierarchical classification. arXiv preprint arXiv:1603.00772.
Naik, A., & Rangwala, H. (2016b). Embedding feature selection for large-scale hierarchical classification. In 2016 IEEE Inter-national Conference on Big Data (Big Data) (pp. 1212-1221). IEEE.
Naik, A., & Rangwala, H. (2018). Large scale hierarchical classification: state of the art. Springer International Publishing.
Partalas, I., Kosmopoulos, A., Baskiotis, N., Artieres, T., Paliouras, G., Gaussier, E., ... & Galinari, P. (2015). Lshtc: A benchmark for large-scale text classification. arXiv preprint arXiv:1503.08581.
Pavia, S., Piraino, N., Islam, K., Pyayt, A., & Gubanov, M. N. (2022). Hybrid Metadata Classification in Large-scale Struc-tured Datasets. J. Data Intell., 3(4), 460-473.
Pilnenskiy, N., & Smetannikov, I. (2020). Feature selection algorithms as one of the python data analytical tools. Future Inter-net, 12(3), 54.
Ramírez-Corona, M., Sucar, L. E., & Morales, E. F. (2016). Hierarchical multilabel classification based on path evalua-tion. International Journal of Approximate Reasoning, 68, 179-193.
Ristoski, P., & Paulheim, H. (2014). Feature selection in hierarchical feature spaces. In Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings 17 (pp. 288-300). Springer International Publish-ing.
Rong, M., Gong, D., & Gao, X. (2019). Feature selection and its use in big data: challenges, methods, and trends. Ieee Access, 7, 19709-19725.
Roul, R. K., & Sahoo, J. K. (2018). Text categorization using a novel feature selection technique combined with ELM. In Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 3 (pp. 217-228). Springer Singapore.
Silla, C. N., & Freitas, A. A. (2011). A survey of hierarchical classification across different application domains. Data mining and knowledge discovery, 22, 31-72.
Stein, R. A., Jaques, P. A., & Valiati, J. F. (2019). An analysis of hierarchical text classification using word embed-dings. Information Sciences, 471, 216-232.
Vora, S., & Yang, H. (2017, July). A comprehensive study of eleven feature selection algorithms and their impact on text clas-sification. In 2017 Computing Conference (pp. 440-449). IEEE.
Weinberger, K., Dasgupta, A., Langford, J., Smola, A., & Attenberg, J. (2009, June). Feature hashing for large scale multitask learning. In Proceedings of the 26th annual international conference on machine learning (pp. 1113-1120).
Wibowo, W., & Williams, H. E. (2002, November). Strategies for minimising errors in hierarchical web categorisation. In Pro-ceedings of the eleventh international conference on Information and knowledge management (pp. 525-531).
Zhao, F., Huang, Y., Wang, L., & Tan, T. (2015). Deep semantic ranking based hashing for multi-label image retrieval. In Pro-ceedings of the IEEE conference on computer vision and pattern recognition (pp. 1556-1564).
Zhou, D., Xiao, L., & Wu, M. (2011). Hierarchical classification via orthogonal transfer.