%0 Conference Paper %1 aryal2023hierarchical %A Aryal, Aakalpa %A Sharma, Sampanna %A Thapa, Shrawan Kumar %A Bhandari, Sudeep %A Shakya, Aman %A Panday, Sanjeeb Prasad %B Intelligent Sustainable Systems. Proceedings of International Conference on Intelligent Sustainable Systems ICoISS 2023 %D 2023 %E Raj, Jennifer S. %E Perikos, Isidoros %E Balas, Valentina Emilia %I Springer, Singapore %K myown ugc %P 253–267 %R https://doi.org/10.1007/978-981-99-1726-6_19 %T Hierarchical Classification of Disaster News Using Local Classifier Per Parent Node %U https://link.springer.com/chapter/10.1007/978-981-99-1726-6_19 %V 665 %X Though a lot of work has been done to classify news headlines into generic categories like politics, sports, disasters, and so on, very little has been done to further classify a particular class of news into a deeper level of categories (especially in the case of disaster news). So, this paper presents a hierarchical classification of disaster news into appropriate disaster categories, based on their headlines. To demonstrate effectiveness of such classification, a hierarchical classifier, trained using local classifier per parent node approach and in mandatory leaf-node prediction setting, is compared with its flat counterpart on disaster news classification, and no significant difference of performance in accuracy is observed. However, the use of a hierarchical classifier as a substitute is, then, justified by demonstrating its advantage in a non-mandatory leafnode prediction setting, where a significant difference of performance in hP (hierarchical Precision) can be achieved by tuning the stopping criteria of the classifier. %@ 978-981-99-1726-6

@inproceedings{aryal2023hierarchical, abstract = {Though a lot of work has been done to classify news headlines into generic categories like politics, sports, disasters, and so on, very little has been done to further classify a particular class of news into a deeper level of categories (especially in the case of disaster news). So, this paper presents a hierarchical classification of disaster news into appropriate disaster categories, based on their headlines. To demonstrate effectiveness of such classification, a hierarchical classifier, trained using local classifier per parent node approach and in mandatory leaf-node prediction setting, is compared with its flat counterpart on disaster news classification, and no significant difference of performance in accuracy is observed. However, the use of a hierarchical classifier as a substitute is, then, justified by demonstrating its advantage in a non-mandatory leafnode prediction setting, where a significant difference of performance in hP (hierarchical Precision) can be achieved by tuning the stopping criteria of the classifier.}, added-at = {2023-03-10T07:59:30.000+0100}, author = {Aryal, Aakalpa and Sharma, Sampanna and Thapa, Shrawan Kumar and Bhandari, Sudeep and Shakya, Aman and Panday, Sanjeeb Prasad}, biburl = {https://www.bibsonomy.org/bibtex/2193fb24b4bfec7ec17b8fe7cc3dec844/amanshakya}, booktitle = {Intelligent Sustainable Systems. Proceedings of International Conference on Intelligent Sustainable Systems [ICoISS 2023]}, doi = {https://doi.org/10.1007/978-981-99-1726-6_19}, editor = {Raj, Jennifer S. and Perikos, Isidoros and Balas, Valentina Emilia}, eventdate = {3-4, February 2023}, eventtitle = {International Conference on Intelligent Sustainable Systems [ICISS 2023]}, interhash = {ba0b992af1ed1bf115f71c11c71d3345}, intrahash = {193fb24b4bfec7ec17b8fe7cc3dec844}, isbn = {978-981-99-1726-6}, keywords = {myown ugc}, month = {February}, pages = {253–267}, publisher = {Springer, Singapore}, series = {Lecture Notes in Networks and Systems}, timestamp = {2023-06-17T18:10:29.000+0200}, title = {Hierarchical Classification of Disaster News Using Local Classifier Per Parent Node}, url = {https://link.springer.com/chapter/10.1007/978-981-99-1726-6_19}, venue = {Tirunelveli, India}, volume = 665, year = 2023 }