{"citation":{"short":"V. Melnikov, E. Hüllermeier, Machine Learning (2018).","apa":"Melnikov, V., &#38; Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. <i>Machine Learning</i>. <a href=\"https://doi.org/10.1007/s10994-018-5733-1\">https://doi.org/10.1007/s10994-018-5733-1</a>","bibtex":"@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis}, DOI={<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>}, journal={Machine Learning}, author={Melnikov, Vitalik and Hüllermeier, Eyke}, year={2018} }","mla":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” <i>Machine Learning</i>, 2018, doi:<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>.","chicago":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” <i>Machine Learning</i>, 2018. <a href=\"https://doi.org/10.1007/s10994-018-5733-1\">https://doi.org/10.1007/s10994-018-5733-1</a>.","ama":"Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. <i>Machine Learning</i>. 2018. doi:<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>","ieee":"V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” <i>Machine Learning</i>, 2018."},"file":[{"file_name":"OnTheEffectivenessOfHeuristics.pdf","file_id":"5305","content_type":"application/pdf","access_level":"closed","file_size":1482882,"date_updated":"2018-11-02T15:30:57Z","creator":"ups","relation":"main_file","date_created":"2018-11-02T15:30:57Z","success":1}],"year":"2018","author":[{"first_name":"Vitalik","full_name":"Melnikov, Vitalik","last_name":"Melnikov"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"}],"language":[{"iso":"eng"}],"doi":"10.1007/s10994-018-5733-1","publication_identifier":{"issn":["1573-0565"]},"_id":"3402","file_date_updated":"2018-11-02T15:30:57Z","type":"journal_article","title":"On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis","department":[{"_id":"355"}],"publication":"Machine Learning","project":[{"_id":"11","name":"SFB 901 - Subproject B3"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901","_id":"1"}],"date_updated":"2022-01-06T06:59:14Z","status":"public","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."}],"ddc":["000"],"date_created":"2018-06-29T07:44:26Z","has_accepted_license":"1","user_id":"15504"}