{"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/evaluation-of-concept-drift-adaptation-for","title":"Evaluation of concept drift adaptation for acoustic scene classifier based on Kernel Density Drift Detection and Combine Merge Gaussian Mixture Model","arxiv_id":"2105.13220","date":"2021-05-27","proceeding":null,"authors":["Ibnu Daqiqil Id","Masanobu Abe","Sunao Hara"],"abstract":"Based on the experimental results, all concepts drift types have their respective hyperparameter configurations. Simple and gradual concept drift have similar pattern which requires a smaller {\\alpha} value than recurring concept drift because, in this type of drift, a new concept appear continuously, so it needs a high-frequency model adaptation. However, in recurring concepts, the new concept may repeat in the future, so the lower frequency adaptation is better. Furthermore, high-frequency model adaptation could lead to an overfitting problem. Implementing CMGMM component pruning mechanism help to control the number of the active component and improve model performance.","url_abs":"https://arxiv.org/abs/2105.13220v1","url_pdf":"https://arxiv.org/pdf/2105.13220v1.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":"evaluation-of-concept-drift-adaptation-for","repo_url":"https://github.com/ibnudaqiqil/CMGMM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"drift-detection","task_name":"Drift Detection"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}