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Particle swarm optimization for ANFIS interpretability and accuracy

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Abstract

The strength of the adaptive neuro-fuzzy system (ANFIS) involves two contradictory requirements in a common fuzzy modeling problem, i.e. interpretability and accuracy. It is known that simultaneous optimization of accuracy and interpretability will improve performance of the system and avoid over-fitting of data. The objective of this study is the integration of particle swarm optimization (PSO) with ANFIS using modified linguistic and threshold values. This integration is expected to enhance the performance of the ANFIS system in classification problems. PSO is used to tune ANFIS parameters, to improve its classification accuracy. It is also used to find the optimal number of rules and their optimal interpretability. The proposed method has been tested on six standard data sets with different inputs of real and integer data types. The findings indicate that the proposed ANFIS–PSO integration provides a better result for classification, both in interpretability and accuracy.

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Acknowledgments

This research was supported by the Fundamental Research Grant Scheme (FRGS-4F347) and Exploratory Research Grant Scheme (ERGS-4L072), which involve directly my candidature for pursuing PhD research in last 2–3 years. We also would like to thank to Prof. Anca L Ralescu from Department of Computer Science, School of Computing Sciences and Informatics, Cincinnati University, Ohio, USA for her useful comments and language editing which have greatly improved the manuscript.

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Authors and Affiliations

  1. Department of Informatics Engineering, Sriwijaya University, Inderalaya, Sumatera Selatan, 30662, Indonesia

    Dian Palupi Rini

  2. UTM Big Data Centre, Universiti Teknologi Malaysia, Skudai, Johor Bahru, 81310, Malaysia

    Dian Palupi Rini, Siti Mariyam Shamsuddin & Siti Sophiayati Yuhaniz

  3. Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor Bahru, 81310, Malaysia

    Dian Palupi Rini, Siti Mariyam Shamsuddin & Siti Sophiayati Yuhaniz

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  1. Dian Palupi Rini
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  2. Siti Mariyam Shamsuddin
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  3. Siti Sophiayati Yuhaniz
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Correspondence to Dian Palupi Rini.

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Communicated by V. Loia.

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Rini, D.P., Shamsuddin, S.M. & Yuhaniz, S.S. Particle swarm optimization for ANFIS interpretability and accuracy. Soft Comput 20, 251–262 (2016). https://doi.org/10.1007/s00500-014-1498-z

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