User-oriented Assessment of Classification Model Understandability

• This paper reviews methods for evaluating and analyzing the understandability of classification models in the context of data mining. The motivation for this study is the fact that the majority of previous work has focused on increasing the accuracy of models, ignoring user-oriented properties such as comprehensibility and understandability. Approaches for analyzing the understandability of data mining models have been discussed on two different levels: one is regarding the type of the models’ presentation and the other is considering the structure of the models. In this study, we present a summary of existing assumptions regarding both approaches followed by an empirical work to examine the understandability from the user’s point of view through a survey. The results indicate that decision tree models are more understandable than rule-based models. Using the survey results regarding understandability of a number of models in conjunction with quantitative measurements of the complexity of the models, we are able to establish correlation between complexity and understandability of the models.

Ämnesord

SAMHÄLLSVETENSKAP  -- Medie- och kommunikationsvetenskap -- Mänsklig interaktion med IKT (hsv//swe)

SOCIAL SCIENCES  -- Media and Communications -- Human Aspects of ICT (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Classification

Understandability

Evaluation

Publikations- och innehållstyp

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