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Invasive weed classification

  • Advances in Intelligent Data Processing and Analysis
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Abstract

Invasive weed optimization (IWO) is a recently published heuristic optimization technique that resembles other evolutionary optimization methods. This paper proposes a new classification technique based on the IWO algorithm, called the invasive weed classification (IWC), to face the problem of pattern classification for multi-class datasets. The aim of the IWC is to find the set of the positions of the class centers that minimize the multi-objective function, i.e., the optimal positions of the class centers. The classification performance is computed as the percentage of misclassified patterns in the testing dataset achieved by the best plants in terms of fitness performance. The performance of the IWC algorithm, both in terms of classification accuracy and training time, is compared with other commonly used classification algorithms.

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Authors and Affiliations

  1. Department of Energy, Politecnico di Milano, Via Ponzio 34/3, 20133, Milan, Italy

    Roozbeh Razavi-Far & Enrico Zio

  2. Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada

    Roozbeh Razavi-Far

  3. Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, UK

    Vasile Palade

  4. Ecole Centrale Paris, Supelec, Paris, 92295, Chatenay-Malabry Cedex, France

    Enrico Zio

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  1. Roozbeh Razavi-Far
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  2. Vasile Palade
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  3. Enrico Zio
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Correspondence to Vasile Palade.

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Razavi-Far, R., Palade, V. & Zio, E. Invasive weed classification. Neural Comput & Applic 26, 525–539 (2015). https://doi.org/10.1007/s00521-014-1656-3

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