SMOTE is an intelligent interpolation technique. It works by using a sample of real data and generating data points between random points and their nearest neighbors. In this way, SMOTE allows you to focus on points of interest, such as underrepresented classes, and create similar poin...
Current research in downsampling also revolve around combining it with other techniques to create hybrid techniques. One combination is to both downsample and upsample the data to get the benefits of both: SMOTE+Tomek Link, Agglomerative Hierarchical Clustering (AHC), and SPIDER are a few examples...
Why reprex? Getting unstuck is hard. Your first step here is usually to create a reprex, or reproducible example. The goal of a reprex is to package your code, and information about your problem so that others can run it…
Actually, we can even take this a step further. Many machine learning models produce probabilities (as opposed to just predictions) and then use a threshold to convert that probability into a prediction. In other words, you have some rules like: if the probability of being positive is greater...
First, we proposed a classification model based on fused text feature representation and various state-of-the-art machine learning algorithms. Then, we explored the motivation for users to participate and create high-quality knowledge in online Q&A communities in the long term. This is an ...
SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research. 2002;16:321-57. Article Google Scholar Fehr D, Veeraraghavan H, Wibmer A, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad ...
Techniques for handling imbalanced data (e.g., oversampling, undersampling, SMOTE). Once training is complete, admins evaluate the model's performance on the test set to assess its generalization ability and ensure it performs well on unseen data. If the trained model passes these tests, the...
Depending on how much upsampling is desired, repeat steps 3 and 4 using a different nearest neighbor. SMOTE counters the problem of overfitting in random oversampling by adding previously unseen new data to the dataset rather than simply duplicating pre-existing data. For this reason, some researc...