Anomaly Detection Algorithm Ranking via Quantile Distance
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Solution Overview
Problem
In the Internet of Things (IoT), it is challenging to differentiate and detect anomalies in large amounts of unlabeled data from sensors, as existing methods lack effective ranking mechanisms for anomaly detection algorithms, which can impact IoT processes.
Innovation Solution
Implementing a method to rank anomaly detection algorithms by generating data distributions and calculating the distance between quantiles, allowing for the identification of the best-suited algorithm for differentiating anomalies in IoT data, using a combination of quantification methods (M1, M2, M3) and cross-validation approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple anomaly detection algorithms are applied to large amounts of unlabeled IoT data, then the ability to detect anomalies improves, but the complexity of selecting and ranking the best algorithm worsens
Solution Approach 1:
The system performs self-evaluation by automatically ranking anomaly detection algorithms based on their own performance metrics. The ranking mechanism uses quantile distance calculations and cross-validation to enable algorithms to be evaluated and selected autonomously without external intervention, resolving the complexity of algorithm selection while maintaining high detection accuracy
Solution Approach 2:
The patent transforms the algorithm selection problem into a parameter-based ranking system. By changing the evaluation parameters to quantile distances and using cross-validation metrics, the system converts complex algorithm performance comparison into a standardized parameter-based ranking process, simplifying the selection complexity while improving reliability
2Ease of manufacture
If manual analysis methods are used for unlabeled IoT data, then the process is simple to implement, but the time and resources required increase significantly
Solution Approach 1:
The patent replaces manual analysis mechanisms with automated computational mechanisms. The system uses computer-implemented methods with automatic algorithm ranking based on quantile distance calculations and cross-validation, substituting human manual analysis with automated electronic processing, thereby maintaining implementation simplicity while dramatically reducing data analysis time
3Measurement precision
If the distance between quantiles is used for ranking algorithms, then the ranking precision improves, but the computational complexity increases
Solution Approach 1:
The system performs preliminary data distribution generation and quantile calculation before the actual ranking process. By pre-computing the data distributions and quantiles for each algorithm, the system reduces the computational complexity during the ranking phase while maintaining high precision in the final algorithm ranking through cross-validation
Data Source
AI summary
Methods, systems, and computer-readable storage media for ranking anomaly detection algorithms, including operations of receiving a set of unlabeled data from one or more sensors in a plurality of sensors of an internet of things, generating a plurality of data distributions corresponding to the set of unlabeled data by using a plurality of anomaly detection algorithms, and ranking the plurality of anomaly detection algorithms relative to the set of unlabeled data based on a distance between a first quantile and a second quantile of each of the plurality of data distributions.