An Interpretable Classification Rule Mining Algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Cano:2013:INS,
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author = "Alberto Cano and Amelia Zafra and Sebastian Ventura",
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title = "An Interpretable Classification Rule Mining
Algorithm",
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journal = "Information Sciences",
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year = "2013",
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volume = "240",
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pages = "1--20",
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keywords = "genetic algorithms, genetic programming,
Classification, Evolutionary Programming,
Interpretability, Rule Mining",
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ISSN = "0020-0255",
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URL = "http://www.sciencedirect.com/science/article/pii/S0020025513002430",
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DOI = "doi:10.1016/j.ins.2013.03.038",
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size = "20 pages",
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abstract = "Obtaining comprehensible classifiers may be as
important as achieving high accuracy in many real-life
applications such as knowledge discovery tools and
decision support systems. This paper introduces an
efficient Evolutionary Programming algorithm for
solving classification problems by means of very
interpretable and comprehensible IF-THEN classification
rules. This algorithm, called the Interpretable
Classification Rule Mining (ICRM) algorithm, is
designed to Maximo the comprehensibility of the
classifier by minims the number of rules and the number
of conditions. The evolutionary process is conducted to
construct classification rules using only relevant
attributes, avoiding noisy and redundant data
information. The algorithm is evaluated and compared to
9 other well-known classification techniques in 35
varied application domains. Experimental results are
validated using several non-parametric statistical
tests applied on multiple classification and
interpretability metrics. The experiments show that the
proposal obtains good results, improving significantly
the interpretability measures over the rest of the
algorithms, while achieving competitive accuracy. This
is a significant advantage over other algorithms as it
allows to obtain an accurate and very comprehensible
classifier quickly.",
- }
Genetic Programming entries for
Alberto Cano Rojas
Amelia Zafra Gomez
Sebastian Ventura
Citations