Optimal weighted parameters of ensemble convolutional neural networks based on a differential evolution algorithm for enhancing pornographic image classification

• Main Authors: Sarayut Gonwirat, Olarik Surinta
• Format: Article
• Language: English
• Published: Khon Kaen University 2021-07-01
• Series: Engineering and Applied Science Research
• Subjects: pornographic image classification; differential evolution algorithm; mutation strategy; convolutional neural networks; ensemble convolutional neural networks; stacked ensemble learning method; ensemble learning method
• Online Access: https://ph01.tci-thaijo.org/index.php/easr/article/download/243592/166484/
• ISSN: 2539-6161; 2539-6218

Use of ensemble convolutional neural networks (CNNs) has become a more robust strategy to improve image classification performance. However, the success of the ensemble method depends on appropriately selecting the optimal weighted parameters. This paper aims to automatically optimize the weighted parameters using the differential evolution (DE) algorithm. The DE algorithm is applied to the weighted parameters and then assigning the optimal weighted to the ensemble method and stacked ensemble method. For the ensemble method, the weighted average ensemble method is applied. For the stacked ensemble method, we use the support vector machine for the second-level classifier. In the experiments, firstly, we experimented with discovering the baseline CNN models and found the best models on the pornographic image dataset were NASNetLarge with an accuracy of 93.63%. Additionally, three CNN models, including EfficientNetB1, InceptionResNetV2, and MobileNetV2, also obtained an accuracy above 92%. Secondly, we generated two ensemble CNN frameworks; the ensemble learning method, called Ensemble-CNN and the stacked ensemble learning method, called StackedEnsemble-CNN. In the framework, we optimized the weighted parameter using the DE algorithm with six mutation strategies containing rand/1, rand/2, best/1, best/2, current to best/1, and random to best/1. Therefore, the optimal weighted was given to classify using ensemble and stacked ensemble methods. The result showed that the Ensemble-3CNN and StackedEnsemble-3CNN, when optimized using the best/2 mutation strategy, surpassed other mutation strategies with an accuracy of 96.83%. The results indicated that we could create the learning method framework with only 3 CNN models, including NASNetLarge, EfficientNetB1, and InceptionResNetV2.