Publication
Title
Building acceptable classification models
Author
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.
Language
English
Source (journal)
Annals of information systems
Publication
2010
Volume/pages
8(2010), p. 53-74
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
External links
VABB-SHW
Record
Identification
Creation 11.01.2011
Last edited 21.11.2016
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