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.
Bibliographical noteThe full text of this item is not available from the repository.
The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-014-1656-3.
- invasive weed classification
- pattern recognition
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- Research Centre for Computational Science and Mathematical Modelling - Professor in Artificial Intelligence and Data Science
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