Ensemble Anomaly Detection Without Parameter Tuning
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Solution Overview
Problem
Conventional anomaly detection algorithms are difficult for non-specialists to use due to parameter tuning requirements and are specialized to detect only certain types of anomalies, such as global, clustered, or local anomalies, often failing to reliably identify other types.
Innovation Solution
An ensemble anomaly detection mechanism that includes a generalized k-nearest neighbor (GKNN) and generalized local outlier factor (GLOF) algorithms, which scale anomaly scores to detect multiple types of anomalies without requiring parameter tuning, allowing for sensitive detection of global, clustered, and local anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anomaly detection algorithms are used, then they can detect certain types of anomalies, but they require parameter tuning that makes them difficult for non-specialists to use
Solution Approach 1:
The system performs self-service by automatically selecting the optimal k value through cross-validation and anomaly score analysis, eliminating the need for manual parameter tuning by non-specialists while maintaining high detection accuracy
Solution Approach 2:
The system dynamically changes the parameter k based on the data characteristics and anomaly types detected, rather than using a fixed value, allowing adaptive optimization without requiring user expertise in parameter selection
2Measurement precision
If specialized anomaly detection algorithms are used, then they can detect specific types of anomalies well, but they fail to reliably detect other types of anomalies
Solution Approach 1:
The system achieves universality by implementing multiple anomaly detection algorithms (KNN, LOF, Isolation Forest) that can detect different types of anomalies, making a single system capable of handling global, local, and clustered anomalies rather than requiring separate specialized algorithms
Solution Approach 2:
The system dynamically adapts by automatically selecting which algorithm and parameters to use based on the characteristics of the data and the types of anomalies being detected, allowing it to optimize performance for different anomaly types without manual intervention
Data Source
AI summary
Disclosed is an improved approach to implement anomaly detection, where an ensemble detection mechanism is provided. An improvement is provided for the KNN algorithm where scaling is applied to permit efficient detection of multiple categories of anomalies. Further extensions are used to optimize local anomaly detection.