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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) (KAR id:38534)

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.
Official URL
http://www.kdd.org/explorations

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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 03 Mar 2014 18:19 UTC
Last Modified: 16 Nov 2021 10:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/38534 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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