Bulldog Breed Classification Using VGG-19 and Ensemble Learning

Abstract

In image classification, the C4.5, Adaboost, and Gradient Boosting algorithms need another method to extract the image's features in the classification process. This research employs transfer learning with the VGG-19 network for the image's features extraction and transfers the result as a dataset to classify image-based Bulldog breeds. As the classifier to classify the extracted features from the VGG 16 model, we employ three ensemble learning algorithms, namely C4.5, AdaBoost, and Gradient Boost. The training data classification results of the American, English, and French bulldog breeds show that, with a 20-fold cross-validation evaluation, the Gradient Boosting algorithm performs the best, with an accuracy value of 0.958, a precision value of 0.958 and recall value of 0.933. And show the highest accuracy (0.933), precision (0.938), and recall (0.933) in the testing data classification. While in the testing data classification, the Gradient Boosting algorithm scores an accuracy value of 0.933, a precision value of 0.938, and a recall value of 0.933