Comparison of Machine Learning Approaches for Classifying Spinodal Events

• URL: http://arxiv.org/abs/2410.09756v1
• Date: Sun, 13 Oct 2024 07:27:00 GMT
• Title: Comparison of Machine Learning Approaches for Classifying Spinodal Events
• Authors: Ashwini Malviya, Sparsh Mittal,
• Abstract summary: We evaluate state-of-the-art models (MobileViT, NAT, EfficientNet, CNN) alongside several ensemble models (majority voting, AdaBoost) Our findings show that NAT and MobileViT outperform other models, achieving the highest metrics-accuracy, AUC, and F1 score on both training and testing data.
• Score: 3.030969076856776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we compare the performance of deep learning models for classifying the spinodal dataset. We evaluate state-of-the-art models (MobileViT, NAT, EfficientNet, CNN), alongside several ensemble models (majority voting, AdaBoost). Additionally, we explore the dataset in a transformed color space. Our findings show that NAT and MobileViT outperform other models, achieving the highest metrics-accuracy, AUC, and F1 score on both training and testing data (NAT: 94.65, 0.98, 0.94; MobileViT: 94.20, 0.98, 0.94), surpassing the earlier CNN model (88.44, 0.95, 0.88). We also discuss failure cases for the top performing models.

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