{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/kaggle-lshtc4-winning-solution","title":"Kaggle LSHTC4 Winning Solution","arxiv_id":"1405.0546","date":"2014-05-03","proceeding":null,"authors":["Antti Puurula","Jesse Read","Albert Bifet"],"abstract":"Our winning submission to the 2014 Kaggle competition for Large Scale\nHierarchical Text Classification (LSHTC) consists mostly of an ensemble of\nsparse generative models extending Multinomial Naive Bayes. The\nbase-classifiers consist of hierarchically smoothed models combining document,\nlabel, and hierarchy level Multinomials, with feature pre-processing using\nvariants of TF-IDF and BM25. Additional diversification is introduced by\ndifferent types of folds and random search optimization for different measures.\nThe ensemble algorithm optimizes macroFscore by predicting the documents for\neach label, instead of the usual prediction of labels per document. Scores for\ndocuments are predicted by weighted voting of base-classifier outputs with a\nvariant of Feature-Weighted Linear Stacking. The number of documents per label\nis chosen using label priors and thresholding of vote scores. This document\ndescribes the models and software used to build our solution. Reproducing the\nresults for our solution can be done by running the scripts included in the\nKaggle package. A package omitting precomputed result files is also\ndistributed. All code is open source, released under GNU GPL 2.0, and GPL 3.0\nfor Weka and Meka dependencies.","url_abs":"http://arxiv.org/abs/1405.0546v2","url_pdf":"http://arxiv.org/pdf/1405.0546v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"random-search","method_name":"Random Search"}],"datasets_introduced":[{"slug":"mall-customer","name":"Online retail dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}