{"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/combining-static-and-dynamic-features-for","title":"Combining Static and Dynamic Features for Multivariate Sequence Classification","arxiv_id":"1712.08160","date":"2017-12-20","proceeding":null,"authors":["Anna Leontjeva","Ilya Kuzovkin"],"abstract":"Model precision in a classification task is highly dependent on the feature\nspace that is used to train the model. Moreover, whether the features are\nsequential or static will dictate which classification method can be applied as\nmost of the machine learning algorithms are designed to deal with either one or\nanother type of data. In real-life scenarios, however, it is often the case\nthat both static and dynamic features are present, or can be extracted from the\ndata. In this work, we demonstrate how generative models such as Hidden Markov\nModels (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can\nbe used to extract temporal information from the dynamic data. We explore how\nthe extracted information can be combined with the static features in order to\nimprove the classification performance. We evaluate the existing techniques and\nsuggest a hybrid approach, which outperforms other methods on several public\ndatasets.","url_abs":"http://arxiv.org/abs/1712.08160v1","url_pdf":"http://arxiv.org/pdf/1712.08160v1.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":[{"paper_slug":"combining-static-and-dynamic-features-for","repo_url":"https://github.com/annitrolla/Generative-Models-in-Classification","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}