Adaptive Outlier Detection Ensembles for Structured Data
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Solution Overview
Problem
Existing automated data processing tools for outlier detection in structured data, such as business ledgers, are inflexible and not optimized for arbitrary new situations, limiting their applicability and performance.
Innovation Solution
A method and system for automatically generating ensembles of analytic computational operations that select features and outlier detection algorithms based on information content, correlations, and user feedback, using machine learning to create customized outlier detection methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed-design ensembles of algorithms are used, then reliability of outlier detection is improved, but adaptability to new situations deteriorates
Solution Approach 1:
The system dynamically generates ensembles by automatically selecting and configuring algorithms based on the specific characteristics of the input data. The ensemble composition is not fixed but adapts to different data types, domains, and outlier patterns, allowing the system to maintain reliability across diverse applications while being highly adaptable to new situations.
Solution Approach 2:
The system changes key parameters of the ensemble including algorithm selection, feature selection, weighting schemes, and threshold values based on data analysis. By automatically adjusting these parameters according to the specific dataset and detection goals, the system achieves both reliability through optimized configurations and adaptability to different data environments.
2Productivity
If purpose-built automated data processing tools are used, then productivity is improved, but adaptability to arbitrary new situations deteriorates
Solution Approach 1:
The system creates a universal outlier detection framework that can handle multiple data types, domains, and outlier patterns through automatic ensemble generation. By incorporating diverse algorithms and automated selection mechanisms, the system achieves high productivity across various applications while maintaining adaptability to arbitrary new situations through its flexible architecture.
Solution Approach 2:
The system performs self-configuration by automatically selecting algorithms, features, and parameters based on the input data characteristics. This self-service capability eliminates the need for manual customization for each new situation, maintaining high productivity while achieving broad adaptability across different data processing tasks.
3Measurement precision
If manually configured ensembles are used, then measurement precision of outlier detection is improved, but loss of time in ensemble creation deteriorates
Solution Approach 1:
The system replaces manual mechanical configuration with automated computational processes. Machine learning algorithms automatically select and configure the ensemble based on data characteristics, achieving measurement precision comparable to manual expert configuration while dramatically reducing the time required for ensemble creation and deployment.
Solution Approach 2:
The system performs preliminary analysis of the input data to automatically determine the optimal ensemble configuration before actual outlier detection begins. By pre-selecting algorithms and parameters based on data characteristics, the system achieves high precision detection while minimizing the time spent on manual configuration and setup.
Data Source
AI summary
A method and apparatus for automatic outlier detection in data sets are provided. An ensemble of outlier detection operations is generated by selecting particular features of the data set, selecting particular algorithms to process those features, and running the selected algorithms using the selected features to identify potential outliers. Feature selection and algorithm selection can be based on a variety of factors, such as measurements of correlation, information content, effectiveness and diversity. Information content may indicate the amount of information in a feature which is a candidate for selection, and may be measured using an information theoretic entropy or potential data compression rate. Diversity and correlation may measure the extent to which different features, algorithms, or combinations thereof produce different information or results.