Comprehensibility of Classification Trees–Survey Design

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.