Title
Building acceptable classification modelsBuilding acceptable classification models
Author
Faculty/Department
Faculty of Applied Economics
Research group
Engineering Management
Publication type
article
Publication
Subject
Economics
Source (journal)
Annals of information systems
Volume/pages
8(2010), p. 53-74
ISSN
1934-3221
vabb
c:vabb:289263
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
Classification (Carvalho et al. Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest. Springer, New York, 2005) is an important data mining task, where the value of a discrete (dependent) variable is predicted, based on the values of some independent variables. Classification models should provide correct predictions on new unseen data instances. This accuracy measure is often the only performance requirement used. However, comprehensibility of the model is a key requirement as well in any domain where the model needs to be validated before it can be implemented. Whenever comprehensibility is needed, justifiability will be required as well, meaning the model should be in line with existing domain knowledge. Although recent academic research has acknowledged the importance of comprehensibility in the last years, justifiability is often neglected. By providing comprehensible, justifiable classification models, they become acceptable in domains where previously such models are deemed too theoretical and incomprehensible. As such, new opportunities emerge for data mining. A classification model that is accurate, comprehensible, and intuitive is defined as acceptable for implementation.
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