Comprehensibility of Classification Trees–Survey Design

By Rok Piltaver, Mitja Lustrek, Matjaz Gams, Sanda Martinčić-Ipšić. 2019

12 citations recorded by the source corpus.

: 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

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Giulia Vilone
2. Longo Luca
3. Xinyi Zhou

In Information Systems

1. Giulia Vilone
2. Longo Luca

Citations in the corpus

• Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “[344] proposed instead to assess the comprehensibility of DTs by asking the participants to perform the following tasks: (I) classify a data-point according to the classification”