@article{2774,
  author = {Rok Piltaver, Mitja Luštrek, Matjaz Gams, Sanda Martincic – Ipšic},
  title = {Comprehensibility of Classification Trees–Survey Design},
  journal = {Information Security Education Journal},
  year = {2019},
  volume = {6},
  number = {1},
  doi = {https://doi.org/10.6025/isej/2019/6/1/15-20},
  url = {http://www.dline.info/isej/fulltext/v6n1/isejv6n1_3.pdf},
  abstract = {Comprehensibility is the decisive factor for application of classifiers in practice. However, most algorithms that
learn comprehensible classifiers use classification model size as a metric that guides the search in the space of all possible
classifiers instead of comprehensibility - which is ill-defined. Several surveys have shown that such simple complexity metrics
do not correspond well to the comprehensibility of classification trees. This paper therefore suggests a classification tree
comprehensibility survey in order to derive an exhaustive comprehensibility metrics better reflecting the human sense of
classifier comprehensibility and obtain new insights about comprehensibility of classification trees.},
}