3 ms·
We assume (like any anomaly detection algorithm) that the majority is normal sample. In your context, the normal samples will be considered as outliers and ther
by siddhartb_ 6y ago
We assume (like any anomaly detection algorithm) that the majority is normal sample. In your context, the normal samples will be considered as outliers and therefore caught by the algorithm. One way to mitigate this is to either swap the labels. Another way is to sample a subset of the anomalies and then try.
- shera 6y agoThank you. Is there a Java implementation available?
- siddhartb_ 6y agoCurrently MIDAS is available in Rust, Python, Ruby and R at https://github.com/bhatiasiddharth/MIDAS https://github.com/bhatiasiddharth/MIDAS. If someone is interested to convert MIDAS to other languages, please feel free to do so and let me know so that I can add a link in the repository.