A Hybrid Model for Pattern Recognition of Marine Turtle Species

Biologists use a manual sea turtle identification key technique to classify marine turtle species according to their scutes patterns. However, limited research to date has focused on developing a system for recognising marine turtle species. Studies in the field of photograph identification system for individual animals have failed to address why none of them developed a system to categorise marine turtle species. The aim of this research is to develop a hybrid model for pattern recognition of marine turtle species based on the stacked generalisation. The hybrid model consists of two major modules: combination unit, which is the combination of the outcomes of neural network model and C4.5 decision tree model, and meta-learning that uses the neural network to aggregate the results from the combination unit and increases the accuracy of the total classification. Several experiments are carried out, where different parameters influencing the overall performance of modules are investigated. The results showed that the trialerror-test could be used to improve the computational cost and mean absolute error of the stacked generalisation when neural networks are used in both combination unit and meta-learning. Therefore, it can be concluded that the hybrid model is an improvement over the traditional manual method for categorization of marine turtle species.