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Comprehensible classification models - a position paper

Freitas, Alex A. (2013) Comprehensible classification models - a position paper. ACM SIGKDD Explorations, 15 (1). pp. 1-10. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
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Abstract

The vast majority of the literature evaluates the performance of classification models using only the criterion of predictive accuracy. This paper reviews the case for considering also the comprehensibility (interpretability) of classification models, and discusses the interpretability of five types of classification models, namely decision trees, classification rules, decision tables, nearest neighbors and Bayesian network classifiers. We discuss both interpretability issues which are specific to each of those model types and more generic interpretability issues, namely the drawbacks of using model size as the only criterion to evaluate the comprehensibility of a model, and the use of monotonicity constraints to improve the comprehensibility and acceptance of classification models by users.

Item Type: Article
Uncontrolled keywords: data mining, machine learning, classificatoin, decision tree, rule induction, decision table, nearest neighbors, Bayesian network classifiers, monotonicity constraints
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 03 Mar 2014 18:19 UTC
Last Modified: 29 May 2019 11:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/38534 (The current URI for this page, for reference purposes)
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