As known, the traditional machine learning algorithms perform poorly on the imbalanced classification, usually ignoring the few samples in the minority class to achieve a good overall accuracy. In t h is post, I will primarily address data augmentation with regard to the. I welcome any ideas and feedback on this topic. Few-shot imbalanced classification tasks are commonly faced in the real-world applications due to the unbalanced data distribution and few samples of rare classes. K-Means SMOTE is an oversampling method for class-imbalanced data. It is to be published as part of a larger project and I just want to make sure that it stands up to the review process. My question now: Is it a good idea to use data augmentation to balance out the dataset, even though it produces a well performing model? Or do I acutally don't need a balanced dataset as long as my model performs to my satisfaction? I also tested different amounts of augmentation based on the resulting performance of the model. score: masks are mostly composed by background pixels (imbalanced-class problem). Net, these transformations include feature normalization, data augmentation, etc. It performs pretty well and is able to generalize the problem satisfactorily. During training phase, data augmentation techniques such as rotation. The ColumnTransformer is a class in the scikit-learn Python machine. Bacause of the data origin, I cannot use common augmentation algorithms, so we developed our own. It consists of eleven classes that vary from 60 events to 7 events per class. The dataset is not balanced, due to experimental reasons.
![keras data augmentation for unbalanced class keras data augmentation for unbalanced class](https://stepup.ai/content/images/2020/07/grid4x3-2.png)
![keras data augmentation for unbalanced class keras data augmentation for unbalanced class](https://images.deepai.org/publication-preview/imbalanced-data-learning-by-minority-class-augmentation-using-capsule-adversarial-networks-page-8-medium.jpg)
While looking at the example here and also Keras documentation, I found the training folder contains equal number of images for each class by default. We typically call this method layers data augmentation due to the fact that the Sequential class we use for data augmentation is the same class we use for implementing sequential neural networks (e.g., LeNet, VGGNet, AlexNet). For class two, each time I extract 500 images in sequence from 1000000 image dataset and probably no data augmentation needed. I'm working with a self generated dataset that is comprised of only 340 'datapoints'. Incorporating data augmentation into a tf.data pipeline is most easily achieved by using TensorFlow’s preprocessing module and the Sequential class. I have a question about a topic that's been discussed many times before, but to which I could find a satisfying answer.