Contents
How to train neural networks on imbalanced data sets?
— Training Deep Neural Networks on Imbalanced Data Sets, 2016. This training procedure can be modified so that some examples have more or less error than others. The misclassification costs can also be taken in account by changing the error function that is being minimized.
Why are neural networks not well suited for deep learning?
Given the balanced focus on misclassification errors, most standard neural network algorithms are not well suited to datasets with a severely skewed class distribution. Most of the existing deep learning algorithms do not take the data imbalance problem into consideration.
How big is my imbalanced training dataset?
My training dataset is a very imbalanced dataset (and so will be the test set considering my problem). The proportion of the imbalanced dataset is 1000:4 , with label ‘0’ appearing 250 times more than label ‘1’. However, I have a lot of training samples : around 23 millions.
Are there any deep learning algorithms that take account of data imbalance?
Most of the existing deep learning algorithms do not take the data imbalance problem into consideration. As a result, these algorithms can perform well on the balanced data sets while their performance cannot be guaranteed on imbalanced data sets.
How is machine learning used to solve class imbalance?
Before getting into the solution of class imbalance lets take a quick peek into what is class imbalance. A machine learning algorithm learns from labelled datasets. Neural networks are primarily used for classification tasks where the network learns by looking at data points belonging to different classes.
How to make a neural network misclassify an example?
If you are using a neural network followed by softmax+cross-entropy or Hinge Loss you can as @chasep255 mentionned make it more costly for the network to misclassify the example that appear the less. To do that simply split the cost into two parts and put more weights on the class that have fewer examples. With \\alpha greater than 1.
How to solve the class imbalance problem in CNN?
Random minority oversampling Random majority undersampling Thresholding with prior class probabilities Oversampling with thresholding Undersampling with thresholding 3. Datasets used for experiment Imbalance was created synthetically. 4. Evaluation metrics and testing The accuracy metric is misleading with imbalanced dataset.