You were asked to investigate failures of a production line component based on sensor readings.
After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents.
You have tried to train several classification models, but none of them converge.
How should you resolve the class imbalance problem?
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A. B. C. D.B.
https://towardsdatascience.com/convolution-neural-networks-a-beginners-guide-implementing-a-mnist-hand-written-digit-8aa60330d022The class imbalance problem arises when the number of instances in one class (in this case, the negative examples) is significantly higher than the other (positive examples). In such a scenario, classification models tend to be biased towards the majority class, leading to poor performance on the minority class.
In this question, the dataset has less than 1% of positive examples, which indicates a severe class imbalance problem. Training classification models on such datasets can be challenging as they may not converge, i.e., the loss function does not improve or stabilize. Therefore, it is necessary to address this class imbalance issue before training the models.
Option A suggests generating additional positive examples by using the class distribution. This approach may not be practical in most real-world scenarios, as it is often challenging or expensive to obtain more positive examples. Additionally, it is unclear how generating additional positive examples from the existing class distribution will help to address the class imbalance problem.
Option B suggests using a convolutional neural network with max pooling and softmax activation. While this may be a valid approach for classification, it does not address the underlying class imbalance problem. Using a more complex model does not guarantee better results in such scenarios.
Option C suggests downsampling the data with upweighting to create a sample with 10% positive examples. Downsampling involves randomly removing negative examples until the number of positive and negative examples is balanced. However, this approach can lead to the loss of crucial information and reduce the overall dataset size, leading to poor model performance. Therefore, it is not recommended.
Option D suggests removing negative examples until the numbers of positive and negative examples are equal. While this approach may work for small datasets, removing a large number of negative examples may lead to a loss of valuable information, resulting in poor model performance.
A more appropriate approach to resolving class imbalance problems is by using techniques such as oversampling or undersampling. Oversampling involves generating synthetic positive examples by replicating the existing ones or creating new ones based on existing data. This approach can help to balance the class distribution and improve model performance.
Undersampling involves randomly removing negative examples until the number of positive and negative examples is balanced. However, as mentioned earlier, this approach can lead to the loss of critical information and reduce the dataset size. Therefore, a better approach would be to use a combination of oversampling and undersampling techniques to balance the dataset and train the classification models. For example, one could use SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic positive examples and random undersampling to balance the class distribution.
In conclusion, option C and D are not recommended, and options A and B do not address the underlying class imbalance problem. The appropriate approach is to use oversampling and/or undersampling techniques to balance the dataset and improve model performance.