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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Aris Kosmopoulos
- sl:arxiv_num : 1306.6802
- sl:arxiv_published : 2013-06-28T11:49:53Z
- sl:arxiv_summary : Hierarchical classification addresses the problem of classifying items into a
hierarchy of classes. An important issue in hierarchical classification is the
evaluation of different classification algorithms, which is complicated by the
hierarchical relations among the classes. Several evaluation measures have been
proposed for hierarchical classification using the hierarchy in different ways.
This paper studies the problem of evaluation in hierarchical classification by
analyzing and abstracting the key components of the existing performance
measures. It also proposes two alternative generic views of hierarchical
evaluation and introduces two corresponding novel measures. The proposed
measures, along with the state-of-the art ones, are empirically tested on three
large datasets from the domain of text classification. The empirical results
illustrate the undesirable behavior of existing approaches and how the proposed
methods overcome most of these methods across a range of cases.@en
- sl:arxiv_title : Evaluation Measures for Hierarchical Classification: a unified view and novel approaches@en
- sl:arxiv_updated : 2013-07-01T17:33:58Z
- sl:bookmarkOf : https://arxiv.org/abs/1306.6802
- sl:creationDate : 2020-09-01
- sl:creationTime : 2020-09-01T23:46:48Z
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