Feature selection for Malaysian medicinal plant leaf shape identification and classification

Malaysian medicinal plants may be abundant natural resources but there has not been much research done on preserving the knowledge of these medicinal plants which enables general public to know the leaf using computing capability. Therefore, in this preliminary study, a novel framework in order to identify and classify tropical medicinal plants in Malaysia based on the extracted patterns from the leaf is presented. The extracted patterns from medicinal plant leaf are obtained based on several angle features. However, the extracted features create quite large number of attributes (features), thus degrade the performance most of the classifiers. Thus, a feature selection is applied to leaf data and to investigate whether the performance of a classifier can be improved. Wrapper based genetic algorithm (GA) feature selection is used to select the features and the ensemble classifier called Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) is used as a classifier. The performance of the feature selection is compared with two feature selections from Weka. In the experiment, five species of Malaysian medicinal plants are identified and classified in which will be represented by using 65 images. This study is important in order to assist local community to utilize the knowledge and application of Malaysian medicinal plants for future generation.