Anomaly Explanation Vector for Interpretable ML Detection
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Solution Overview
Problem
Existing anomaly detection methods using machine learning algorithms struggle to provide interpretable explanations for anomaly detection results, especially when dealing with complex time series data from sensors, leading to a lack of trust and understanding in the underlying models.
Innovation Solution
A computer-implemented anomaly determination explanation method that determines an explanation vector by calculating an anomaly measure from an input vector using a state evaluation model, normalizing the input vector to represent a normal state, and then estimating the contribution of each component to the anomaly determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are used for anomaly detection, then detection accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent segments the anomaly detection system into two distinct components: (1) a machine learning-based anomaly detection module that maintains high detection accuracy, and (2) a separate explanation generation module that produces human-interpretable explanations. This segmentation allows each component to optimize for its specific function without compromising the other.
Solution Approach 2:
The patent introduces an intermediary explanation generation mechanism that acts as a bridge between the black-box anomaly detection model and human users. This intermediary translates the model's internal decision logic into comprehensible explanations, thereby maintaining both detection accuracy and interpretability.
2Reliability
If complex time series data is analyzed, then anomaly detection capability is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and processes only the essential features from complex time series data that are most relevant for anomaly detection. By selectively extracting key temporal patterns and dependencies rather than processing all raw data, the system maintains high detection capability while reducing computational burden.
3Reliability
If explanations are generated for anomaly detection, then user trust is improved, but computational overhead increases
Solution Approach 1:
The patent implements a partial explanation generation approach where explanations are produced only for the specific features that contributed to anomaly detection, rather than generating comprehensive explanations for all input features. This selective explanation strategy reduces computational overhead while still providing sufficient interpretability to build user trust.
Data Source
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AI summary
The invention relates to a method and a device for determining reasons for detection of an anomalous system state. To this end, a normalized system state is determined as being the closest state to the anomalous system state that is determined not to be an anomalous system state.