How do you incorporate domain knowledge or expert feedback into hidden Markov models for anomaly detection?
Hidden Markov models (HMMs) are powerful tools for anomaly detection, as they can capture the sequential patterns and dependencies of data over time. However, HMMs are also prone to overfitting and misclassification, especially when the data is noisy, sparse, or complex. How can you improve the performance and accuracy of your HMMs by incorporating domain knowledge or expert feedback? In this article, we will explore some methods and examples of how to do so.