Outlier Detection Program Selection via Dataset Meta-Features
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Solution Overview
Problem
Existing outlier-detection systems require manual selection and evaluation of programs, which is computationally expensive and time-consuming, especially for large datasets, and often rely on labeled data that is rare or costly to obtain, limiting their utility in dynamic environments.
Innovation Solution
A computing system uses meta-features of datasets to generate a performance vector by multiplying an embedding vector with a reference V matrix, allowing for the selection of an outlier-detection program with high estimated performance without manual labeling or extensive testing, thereby reducing computational complexity and manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection and evaluation of outlier-detection programs is performed, then the suitability of the detector for the dataset can be assessed, but the process becomes computationally expensive and time-consuming
Solution Approach 1:
The system pre-computes and stores performance metrics for multiple outlier-detection programs across various dataset types in a lookup table during an offline phase. When a new dataset arrives, the system quickly queries this pre-computed table using meta-features to identify the most suitable detector, avoiding the need for time-consuming real-time evaluation of each detector on the new data.
Solution Approach 2:
Instead of evaluating actual detectors on the new dataset, the system uses meta-features (descriptive characteristics) of the dataset to copy or retrieve performance information from a reference table that contains pre-evaluated performance data. This allows the system to estimate detector performance without performing the actual computationally expensive detection process.
2Reliability
If multiple outlier-detection programs are applied and evaluated to identify the best performing one, then the most suitable detector can be selected, but the computational cost increases significantly
Solution Approach 1:
The system extracts only the essential characteristics (meta-features) of the dataset such as data type, dimensionality, and distribution properties, rather than applying full detector evaluations. These extracted meta-features are then used to query a reference table for detector recommendations, significantly reducing computational resources while maintaining reliable selection.
Solution Approach 2:
The reference table serves as a universal lookup mechanism that can provide detector recommendations for multiple different datasets by matching their meta-features. This single pre-computed table replaces the need to individually evaluate and compare multiple detectors for each new dataset, reducing overall computational energy consumption across many detection tasks.
3Measurement precision
If labeled outlier data is used to evaluate and select outlier-detection programs, then the performance of detectors can be accurately assessed, but obtaining labeled data becomes rare and costly
Solution Approach 1:
The system uses unsupervised learning approaches where the outlier-detection programs evaluate datasets without requiring manually labeled outlier annotations. The meta-features and performance metrics are derived automatically from the raw data characteristics, allowing the system to self-assess detector performance without external labeling resources.
Solution Approach 2:
The reference table is pre-populated with performance data obtained from evaluating detectors on various datasets with known characteristics. This preliminary evaluation phase stores the results for future queries, eliminating the need to perform time-consuming labeled data preparation and evaluation for each new detection task.
4Adaptability or versatility
If outlier-detection programs are frequently re-evaluated for changing datasets, then the detectors remain suitable for current data characteristics, but the computational expense accumulates over time
Solution Approach 1:
The system continuously monitors meta-features of incoming datasets and compares them against stored profiles in the reference table. When significant changes in data characteristics are detected, the system queries the reference table to retrieve updated detector recommendations, providing feedback-driven adaptation without performing full re-evaluations of all detectors.
Data Source
AI summary
Embodiments described herein involve selecting outlier-detection programs that are specific to meta-features of datasets. For instance, a computing system constructs a performance vector from a U vector and a reference V matrix. Vector elements of the performance vector identify estimated performance values of various outlier-detection programs with respect to an input dataset. The U vector is generated using meta-features of the input dataset. The reference V matrix is generated from a training process in which performance values of the various outlier-detection programs with respect to training input datasets are used to obtain the reference V matrix via a UV decomposition. The computing system selects an outlier-detection program having a greater estimated performance value in the performance vector as compared to other outlier-detection programs' respective estimated performance values.