Howtosignalprocessing
- How do you handle imbalanced time series data?
- What is the problem with imbalanced data?
- What's the best approach to handle an imbalanced dataset?
How do you handle imbalanced time series data?
A common solution for the general problem of mining imbalanced data sets is to resort to resampling strategies. These strategies change the distribution of learning data in order to balance the number of rare and normal cases, attempting to reduce the skewness of the data.
What is the problem with imbalanced data?
Imbalanced data is a common problem in machine learning, which brings challenges to feature correlation, class separation and evaluation, and results in poor model performance.
What's the best approach to handle an imbalanced dataset?
A widely adopted and perhaps the most straightforward method for dealing with highly imbalanced datasets is called resampling. It consists of removing samples from the majority class (under-sampling) and/or adding more examples from the minority class (over-sampling).
