An Interpretable Classification Rule Mining Algorithm

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@Article{Cano:2013:INS,
  author =       "Alberto Cano and Amelia Zafra and Sebastian Ventura",
  title =        "An Interpretable Classification Rule Mining
                 Algorithm",
  journal =      "Information Sciences",
  year =         "2013",
  volume =       "240",
  pages =        "1--20",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Evolutionary Programming,
                 Interpretability, Rule Mining",
  ISSN =         "0020-0255",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020025513002430",
  DOI =          "doi:10.1016/j.ins.2013.03.038",
  size =         "20 pages",
  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