Comprehensibility of classification trees – survey design (CROSBI ID 615155)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Piltaver, Rok ; Luštrek, Mitja ; Gams, Matjaž ; Martinčić–Ipšić, Sanda
engleski
Comprehensibility of classification trees – survey design
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.
classification tree; comprehensibility; survey
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Podaci o prilogu
70-74.
2014.
objavljeno
Podaci o matičnoj publikaciji
Intelligent Systems-IS 2014 (Volume A)
Piltaver, Rok ; Gams, Matjaž
Ljubljana: Institut Jožef Stefan
Podaci o skupu
Intelligent Systems 2014
predavanje
07.10.2014-08.10.2014
Ljubljana, Slovenija