Sungho Suh

(AG Embedded Intelligence, Prof. Lukowicz)
hosted by PhD Program in CS @ TU KL

"Improving Classification Performance under Imbalanced Data Conditions"

The data imbalance problem for classification is a frequent but challenging task. In a real-world dataset, most of the class distribution is imbalanced and the classification result under imbalanced data condition induces a bias in the majority data class. The data imbalanced problem is easily discovered in many domains like computer vision, medical diagnosis, fault detection, and so on. For fault detection, the normal condition data are more common than faulty condition data in real manufacturing environments. This dissertation investigates using data augmentation to improve the performance of the classification under imbalanced data conditions. I propose a generative oversampling method on bearing fault detection on induction motor using Generative Adversarial Networks (GANs) and the data augmentation method for four benchmark dataset, MNIST, EMNIST, Fashion MNIST, and CIFAR-10. Additionally, I propose robust shipping label recognition and validation for logistics by using object detection. In this talk, I will explain the extends versions of GANs, the on-going framework of the fault detection algorithm, the results of the proposed method, and the research plan.

Time:	Tuesday, 16.07.2019, 13:45
Place:	48-680
Video: