Combining Instance-Based Learning and Logistic Regression for Multilabel Classification

@inproceedings{Cheng2009CombiningIL,
  title={Combining Instance-Based Learning and Logistic Regression for Multilabel Classification},
  author={Weiwei Cheng and Eyke H{\"u}llermeier},
  booktitle={ECML/PKDD},
  year={2009}
}
Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for conventional classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and interdependencies between labels into account, their potential has not yet… Expand
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