{"ddc":["000"],"_id":"3402","type":"journal_article","abstract":[{"lang":"eng","text":"In machine learning, so-called nested dichotomies are utilized as a reduction technique, i.e., to decompose a multi-class classification problem into a set of binary problems, which are solved using a simple binary classifier as a base learner. The performance of the (multi-class) classifier thus produced strongly depends on the structure of the decomposition. In this paper, we conduct an empirical study, in which we compare existing heuristics for selecting a suitable structure in the form of a nested dichotomy. Moreover, we propose two additional heuristics as natural completions. One of them is the Best-of-K heuristic, which picks the (presumably) best among K randomly generated nested dichotomies. Surprisingly, and in spite of its simplicity, it turns out to outperform the state of the art."}],"language":[{"iso":"eng"}],"project":[{"name":"SFB 901 - Subproject B3","_id":"11"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901","_id":"1"}],"doi":"10.1007/s10994-018-5733-1","date_updated":"2022-01-06T06:59:14Z","file_date_updated":"2018-11-02T15:30:57Z","department":[{"_id":"355"}],"status":"public","file":[{"content_type":"application/pdf","access_level":"closed","file_size":1482882,"creator":"ups","relation":"main_file","file_id":"5305","date_updated":"2018-11-02T15:30:57Z","success":1,"date_created":"2018-11-02T15:30:57Z","file_name":"OnTheEffectivenessOfHeuristics.pdf"}],"citation":{"bibtex":"@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis}, DOI={10.1007/s10994-018-5733-1}, journal={Machine Learning}, author={Melnikov, Vitalik and Hüllermeier, Eyke}, year={2018} }","apa":"Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1","chicago":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018. https://doi.org/10.1007/s10994-018-5733-1.","ama":"Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1","ieee":"V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018.","short":"V. Melnikov, E. Hüllermeier, Machine Learning (2018).","mla":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018, doi:10.1007/s10994-018-5733-1."},"year":"2018","user_id":"15504","author":[{"full_name":"Melnikov, Vitalik","last_name":"Melnikov","first_name":"Vitalik"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"publication_identifier":{"issn":["1573-0565"]},"publication":"Machine Learning","has_accepted_license":"1","title":"On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis","date_created":"2018-06-29T07:44:26Z"}