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A description of several characteristics for improving the accuracy and interpretability of inductive linguistic rule learning algorithms

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Accuracy Improvements in Linguistic Fuzzy Modeling

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 129))

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Abstract

The learning algorithms can be an useful tool for helping to the humans to understand the behavior of phenomena from a set of samples. In particular, those algorithms that represent the knowledge obtained by linguistic fuzzy rules are appropriate for this task. However, it is not sufficient that the knowledge representation is close to the humans comprehension. Furthermore, it is necessary that the knowledge is expressed as simple as possible.

In this chapter, we classify some techniques, models and tools for improving the knowledge obtained by inductive linguistic rule learning algorithms from three different points of view: those that increase the knowledge interpretability, those that increase the knowledge accuracy keeping its interpretability and those that simultaneously increase the accuracy and interpretability of the knowledge.

In this study, we have considered fuzzy rules expressed by the Disjunctive Normal Form (DNF).

This work has been supported by the CICYT under Project TAP99-0535-C02-01

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Authors and Affiliations

  1. Departamento de Ciencias de la Computación e Inteligencia Artificial. E.T.S. de Ingeniería Informática, Universidad de Granada, 18071, Granada, Spain

    Eugenio Aguirre, Antonio Gonzalez & Raúl Pérez

Authors
  1. Eugenio Aguirre
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  2. Antonio Gonzalez
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  3. Raúl Pérez
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Editors and Affiliations

  1. Dpto. Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería Informática, Universidad de Granada, E - 18071, Granada, Spain

    Jorge Casillas , Oscar Cordón  & Francisco Herrera ,  & 

  2. Dpto. Matemáticas Aplicadas a las Tecnologías de la Información, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, E - 28040, Madrid, Spain

    Luis Magdalena

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Aguirre, E., Gonzalez, A., Pérez, R. (2003). A description of several characteristics for improving the accuracy and interpretability of inductive linguistic rule learning algorithms. In: Casillas, J., Cordón, O., Herrera, F., Magdalena, L. (eds) Accuracy Improvements in Linguistic Fuzzy Modeling. Studies in Fuzziness and Soft Computing, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37058-1_11

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  • DOI: https://doi.org/10.1007/978-3-540-37058-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05703-8

  • Online ISBN: 978-3-540-37058-1

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