Comprehensibility of Classification Treesâ€“Survey Design
Rok Piltaver, Mitja LuÅ¡trek, Matjaz Gams, Sanda Martincic â€“ IpÅ¡ic
Information Security Education Journal, Vol. 6, No. 1, 2019.
DOI: https://doi.org/10.6025/isej/2019/6/1/15-20
Link: 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.