Rare Event Detection Ensemble Using Segmented Training Partitions
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Solution Overview
Problem
Current methods fail to accurately detect rare events, such as cyber-attacks or electrical-grid malfunctions, due to the lack of effective tools for identifying these low-frequency occurrences in a timely manner.
Innovation Solution
A system comprising a processor and memory that generates an ensemble of machine-learning models using training partitions, including an event partition and non-event partitions, to detect rare events by processing new data and updating the ensemble with unsupervised models, facilitated by an interactive graphical user interface for customization and deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used, then the system is simple to operate, but the detection accuracy of rare events is poor
Solution Approach 1:
The training data is segmented into multiple partitions, with the majority class (non-event observations) divided into several subsets. Each machine learning model is trained on a different partition, allowing the system to process and learn from large datasets in manageable segments while maintaining detection accuracy for rare events.
Solution Approach 2:
Multiple machine learning models are combined into an ensemble where their predictions are aggregated. The ensemble merges the outputs of individual models trained on different data partitions, producing a consolidated detection result that leverages the strengths of each model while reducing individual model limitations.
2Measurement precision
If ensemble of machine-learning models is generated using training partitions, then the detection accuracy of rare events is improved, but the computational resources and time required increase
Solution Approach 1:
The large training dataset is divided into multiple smaller partitions, allowing parallel training of multiple models simultaneously. This segmentation enables efficient utilization of computational resources and reduces the time required to train each individual model while maintaining overall detection accuracy through the ensemble approach.
Solution Approach 2:
The system performs preliminary actions by pre-processing and partitioning the training data before model training begins. This preliminary organization of data into structured partitions optimizes the training process and enables more efficient computation when the ensemble models are deployed for detection.
3Measurement precision
If advanced machine-learning models are deployed, then the detection capability is improved, but the ease of operation decreases due to technical expertise requirements
Solution Approach 1:
The system performs self-service by automatically handling data partitioning, model training, and ensemble configuration without requiring user intervention. The graphical user interface presents simplified options that automatically trigger complex backend processes, allowing users to deploy advanced detection systems without needing to understand the underlying machine learning complexity.
Solution Approach 2:
The graphical user interface acts as an intermediary between the user and the complex machine learning system. It translates simple user actions into sophisticated model training and deployment operations, shielding users from technical complexity while enabling access to advanced detection capabilities.
Data Source
AI summary
A system can obtain observations from a dataset. The system can generate a set of training partitions based on the observations and generate an ensemble of machine-learning models based on the set of training partitions. The system can then receive new data and detect whether the new data is indicative of the event using the ensemble. In some cases, the system can update the ensemble by providing the new data as input to an unsupervised machine-learning model that is separate from the ensemble of machine-learning models; receiving an output from the unsupervised machine-learning model indicating whether or not the new data is indicative of the event; incorporating a new observation into the dataset indicating whether or not the new data is indicative of the event based on the output from the unsupervised machine-learning model; and updating the ensemble based on the dataset with the new observation.