Bayesian Anomaly Detection and Classification for Noisy Data
| Autor: | Ethan Roberts, Bruce A. Bassett, Michelle Lochner |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Bayesian probability Pattern recognition 02 engineering and technology Formalism (philosophy of mathematics) symbols.namesake Gaussian noise Simulated data 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Bayesian framework Anomaly detection Standard algorithms Artificial intelligence business Noisy data |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030493417 ISDA |
| DOI: | 10.1007/978-3-030-49342-4_41 |
| Popis: | Statistical uncertainties are rarely incorporated into machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise as an example, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|