On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis

@article{Melnikov2018OnTE,
  title={On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis},
  author={Vitalik Melnikov and Eyke H{\"u}llermeier},
  journal={Machine Learning},
  year={2018},
  volume={107},
  pages={1537-1560}
}
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… CONTINUE READING
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