Comparison of Ensemble Methods for Malaysian Medicinal Leaf Images Identification and Classification

  • Authors

    • Mohd Shamrie Sainin
    • Faudziah Ahmad
    • Rayner Alfred
    https://doi.org/10.14419/ijet.v7i3.20.27340
  • Medicinal plant, ensemble, AdaboostM1, Random Forest

  • Abstract

    Malaysia has abundant natural resources especially plants which can be used for medicinal or herbal purposes. However, there is less research to preserve the knowledge of these resources to be utilized by the community in identifying useful medicinal plants using computing tools. In order to support this study, finding suitable method for identification and classification must be done in order to provide better classification performance. Ensemble methods are classification methods that combines several diverse classifiers which known to perform better than single classifiers. In this regard, the best ensemble method for this specific leaf image data need to be explored and Weka has been used as the platform to compare related ensemble methods. The study in this paper compares several ensemble classifiers where AdaboostM1 with Random Forest as base classifier provides the best technique to the nature of the shape-based Malaysian medicinal leaf images data. The ensemble classifier is also tested with other shape based dataset image domain and shows that the classifier is able to produce the best classification performance.

     

     

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  • How to Cite

    Shamrie Sainin, M., Ahmad, F., & Alfred, R. (2018). Comparison of Ensemble Methods for Malaysian Medicinal Leaf Images Identification and Classification. International Journal of Engineering & Technology, 7(3.20), 785-789. https://doi.org/10.14419/ijet.v7i3.20.27340

    Received date: 2019-02-12

    Accepted date: 2019-02-12