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Abstract: Leaf images sample for Malaysian Medicinal Plant classification. Previous study using this dataset are using randomly selected training (45 images) and testing (20 images). Images are pre-processed and numeric values using shape-based features are extracted. Preliminary study using several classifiers such as SMO, J48, NB, k-NN and Random Forest show that SMO produced 50% accuracy.
Number of Instances: 65
Number of Attributes: -
Number of Classes: 5
Associated Tasks: Classification
Mohd Shamrie Sainin, Rayner Alfred, Suraya Alias and Mohamed A.M., Lammasha. (2018) Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning. In: Knowledge Management International Conference (KMICe) 2018, 25 –27 July 2018, Miri Sarawak, Malaysia.
Sainin M.S., Alfred R., Adnan F., Ahmad F. (2018) Combining Sampling and Ensemble Classifier for Multiclass Imbalance Data Learning. In: Alfred R., Iida H., Ag. Ibrahim A., Lim Y. (eds) Computational Science and Technology. ICCST 2017. Lecture Notes in Electrical Engineering, vol 488. Springer, Singapore
Mohd Shamrie Sainin, Rayner Alfred, Faudziah Ahmad, Mohamed A.M. Lammasha (2017). An Evaluation of Feature Selection methods on Multi-Class Imbalance and High Dimensionality Shape-Based Leaf Images Features. Journal of Telecommunication Electronic and Computer Engineering. 9(1-2) 57-61.
Mohd Shamrie Sainin, Faudziah Ahmad and Rayner Alfred. (2016). Comparison of Half and Full-leaf Feature Extraction for Malaysian Medicinal Leaf Identification and Classification (Extended Version), Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia, Vol 39(9), 31-39.
Mohd Shamrie Sainin, and Rayner Alfred. (2014). Feature Selection for Malaysian Medicinal Plant Leaf Shape Identification and Classification. Proceedings of the International Conference on Computational Science and Technology – 2014 (ICCST’14), IEEE, pp 1-6.
Mohd Shamrie Sainin, Taqiah Khadijah Ghazali and Rayner Alfred. (2014). Malaysian Medicinal Plant Leaf Shape Identification and Classification. In proceeding of: Knowledge Management International Conference and Exhibition 2014, Langkawi, Malaysia, Vol 2, pp. 578-583. (ISBN: 978-983-2078-92-0).
Sainin, M. S., & Alfred, R., & Ahmad, F. (2021). Ensemble meta classifier with sampling and feature selection for data with imbalance multiclass problem. Journal of Information and Communication Technology, 20(2), 103-133. https://doi.org/10.32890/jict2021.20.2.1