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A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification

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Abstract

Fuzzy rule-based systems (FRBSs) are well-known soft computing methods commonly used to tackle classification problems characterized by uncertainties and imprecisions. We propose a hybrid intelligent fruit fly optimization algorithm (FOA) to generate and classify fuzzy rules and select the best rules in a fuzzy if–then rule system. We combine a FOA and a heuristic algorithm in a hybrid intelligent algorithm. The FOA is used to create, evaluate and update triangular fuzzy rule-based and orthogonal fuzzy rule-based systems. The heuristic algorithm is used to calculate the certainty grade of the rules. The parameters in the proposed hybrid algorithm are tuned using the Taguchi method. An experiment with 27 benchmark datasets and a tenfold cross-validation strategy is designed and carried out to compare the proposed hybrid algorithm with nine different FRBSs. The results show that the hybrid algorithm proposed in this study is significantly more accurate than the nine competing FRBSs.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Seyed Mohsen Mousavi & Najmeh Alikar

  2. Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA, 19141, USA

    Madjid Tavana

  3. Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, 33098, Paderborn, Germany

    Madjid Tavana

  4. Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran

    Mostafa Zandieh

Authors
  1. Seyed Mohsen Mousavi
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  2. Madjid Tavana
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  3. Najmeh Alikar
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  4. Mostafa Zandieh
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Correspondence to Madjid Tavana.

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Mousavi, S.M., Tavana, M., Alikar, N. et al. A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification. Neural Comput & Applic 31, 873–885 (2019). https://doi.org/10.1007/s00521-017-3115-4

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  • DOI: https://doi.org/10.1007/s00521-017-3115-4

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