Minority-Class Data Mining via Recalibration Neural Networks
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Solution Overview
Problem
Existing methods for mining minority-class data samples from skewed datasets are inefficient, often resulting in wasted resources and poor performance, as they fail to accurately identify and label minority-class data samples due to skewed class distributions and reliance on ineffective metrics like entropy or max score.
Innovation Solution
The use of a trained recalibration neural network and anomaly detector, such as an autoencoder, to identify and label minority-class data samples by recalibrating activations and modeling the entire distribution of activations, allowing for more effective mining of minority-class data samples without the limitations of hard-coded metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If naive methods like random sampling are used to mine data samples from the unlabeled data stream, then the mining process is simple to implement, but the class distribution remains skewed and minority-class data samples are not effectively identified
Solution Approach 1:
The patent changes the parameter being measured from simple randomness to uncertainty metrics (entropy, max score) computed from neural network predictions. This transforms the mining process from uniform random sampling to targeted sampling based on model uncertainty, improving minority-class identification while maintaining computational feasibility
Solution Approach 2:
The patent replaces manual or simple random sampling mechanisms with an automated neural network-based uncertainty estimation system. The neural network automatically identifies uncertain predictions that likely correspond to minority-class samples, substituting mechanical random sampling with intelligent model-based selection
2Extent of automation
If common computer-based methods exploiting neural network uncertainty (max score, entropy) are used to mine data samples, then automation is achieved, but performance worsens compared to random sampling in highly skewed distributions
Solution Approach 1:
The patent inverts the traditional approach by not directly using uncertainty metrics to identify minority-class samples. Instead, it uses uncertainty to identify samples for exclusion from minority-class mining, then applies specialized techniques (reconstruction error, activation analysis) to the remaining samples, reversing the logic to improve effectiveness
Solution Approach 2:
The patent introduces intermediary metrics beyond simple uncertainty (reconstruction error from autoencoders, activation pattern analysis) that mediate between automated processing and effective minority-class identification. These intermediaries provide additional signal about sample characteristics that uncertainty alone cannot capture
3Reliability
If more data samples are mined and labeled to balance class distribution, then the training dataset improves, but computational and financial costs increase
Solution Approach 1:
The patent applies local quality by focusing mining efforts specifically on regions of the data space where minority-class samples are likely to exist (high uncertainty regions). Instead of uniformly processing all samples or using expensive oversampling techniques across the entire dataset, it concentrates computational resources on locally identifying and labeling only the most promising minority-class candidates
Solution Approach 2:
The patent uses partial action by mining and labeling only a subset of data samples that are most likely to be minority-class, rather than attempting to label all uncertain samples or using excessive oversampling. This partial approach achieves sufficient class balance improvement at reduced cost
Data Source
AI summary
Methods and systems for mining minority-class data samples are described. A minority-class mining service receives activations generated by an inner-layer of a client neural network that has been trained to perform a prediction task that involves classification. The minority-class mining service generates a recalibrated activation using a recalibration neural network, and generates an anomaly detector output using an anomaly detector. From the anomaly detector output, a minority-class score is computed for the data sample represented by a received activation. The computed minority-class score is compared against a minority-class threshold to identify a candidate minority-class data sample. The candidate minority-class data sample can then be labeled and added to the training dataset for the client neural network.