Semi-Supervised Anomaly Detection System Through Ensemble Stacking Algorithm

Explore a cutting-edge semi-supervised anomaly detection system through ensemble stacking algorithm in this 31-minute video featuring Chuying Ma, a senior data scientist at Walmart Inkiru. Delve into the challenges of detecting anomalies in the retail industry and learn how this innovative system autonomously identifies unknown anomalies and enhances labels for fraud prevention. Discover the system's flexible architecture that ingests diverse transaction data and generates individual anomaly detection models, outperforming traditional methods. Gain insights into the motivation behind anomaly detection, various techniques, and the specific modules of this new system. Examine real-world modeling examples and assess the model's out-of-time (OOT) performance. Benefit from Chuying Ma's expertise in machine learning and fraud prevention as she presents a unified approach to anomaly detection that revolutionizes fraud detection in retail transactions.

- Introduction
- Motivation: What is Anomaly?
- Motivation: Anomaly Detection Techniques
- Challenges of Detecting Anomalies in Retail Industry
- Why This New Anomaly Detection System?
- Modules
- Anomaly Detection Modeling Examples
- Model Performance OOT
- Questions