Auditable Neural Network Quarantine for Selective Data Labeling
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Solution Overview
Problem
In mission critical environments where the risk of false positives or negatives is significant, there is often a lack of sufficient labeled data to prime an accurate machine learning model, leading to high computing-resource consumption and costly manual labeling.
Innovation Solution
A machine learning system that identifies anomalous data points by comparing them to a set of expectations, quarantines those that do not match, and sends them to human analysts for labeling, creating neurons based on the labeled data points to refine the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is performed on all data points to ensure model accuracy, then model accuracy is improved, but computing-resource consumption and cost increase significantly
Solution Approach 1:
The patent segments the data labeling process into two distinct pathways: (1) automated labeling for data points that match existing model expectations, and (2) selective manual labeling only for anomalous data points that deviate from expectations. This segmentation allows the system to maintain high model accuracy while minimizing computing-resource consumption by applying different labeling strategies to different subsets of data.
Solution Approach 2:
The patent applies local quality by treating different data points with different labeling approaches based on their characteristics. Data points that conform to established patterns receive automated labeling, while only the anomalous subset requiring human expertise receives manual labeling. This localized application of labeling resources optimizes both accuracy and resource efficiency.
2Measurement precision
If extensive manual labeling is performed on all data points, then model accuracy is improved, but time consumption increases
Solution Approach 1:
The patent divides the data processing workflow into automated and manual segments. The majority of data points are processed through automated labeling pipelines that operate rapidly, while only the small subset of anomalous data points is routed to manual labeling. This time-efficient segmentation dramatically reduces overall labeling time while preserving model accuracy.
Solution Approach 2:
The system performs preliminary automated labeling and anomaly detection before committing resources to manual labeling. By pre-identifying which data points require human attention, the system avoids the time cost of manual review for all data points, thereby reducing total time consumption while maintaining accuracy.
3Use of energy by moving object
If selective labeling of only anomalous data points is performed, then computing resources are conserved, but model accuracy may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the model continuously learns from manually labeled anomalous data points. The human-labeled anomalies are fed back into the training process, allowing the model to refine its expectations and improve accuracy over time. This feedback loop ensures that selective labeling does not compromise long-term model accuracy.
Solution Approach 2:
The system employs self-service through automated model refinement processes that use the selectively labeled anomalous data to automatically update and improve the model. This self-service capability allows the model to maintain and enhance accuracy without requiring continuous extensive manual labeling of all data points.
Data Source
AI summary
A method for candidate data points selection for labeling unlabeled data points is provided. The method may include inputting a first data point to an auditable neural network. The method may include predicting, using the network, a label for the first data point. The method may include deconstructing, based on a simplicial structure, the first data point into a plurality of component parts of the first data point. The method may include reconstructing, the first data point into a reconstructed first data point, based on the simplicial structure, using the plurality of component parts and the label. The method may include generating a reconstruction error value based on a reconstruction error algorithm that compares the first data point to the reconstructed first data point. The method may include quarantining the first data point within the auditable neural network when the reconstruction error value is above a threshold reconstruction error value.