Active Learning Sample Selection Using Margin Curves
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Solution Overview
Problem
Determining the optimal sample size for training machine learning models is challenging due to the need to balance bias and variance, consider the cost and feasibility of data collection, and account for model complexity and performance metrics.
Innovation Solution
The system generates a margin curve to identify the threshold for optimizing a specific performance metric by plotting predicted probabilities against threshold values, allowing for the selection of an optimal sample size based on user-defined constraints and values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a larger sample size is used for training, then the variance of the model is reduced by providing more representative data, but the bias increases if the data is not diverse enough
Solution Approach 1:
The patent applies local quality by selecting samples with specific characteristics (high expected model variance) rather than uniformly sampling. The uncertainty estimator identifies regions of the feature space where the model is uncertain, and sampling focuses on those specific local areas to reduce overall model variance without introducing bias.
Solution Approach 2:
The patent changes the sampling parameter from uniform distribution to a distribution based on expected model variance. By using the uncertainty estimator to weight sample selection, the system dynamically adjusts which samples are selected based on their contribution to reducing model variance, thereby resolving the contradiction between reducing variance and avoiding bias.
2Productivity
If active learning is used to select informative samples, then training efficiency is improved, but determining the optimal number of samples to select becomes challenging
Solution Approach 1:
The patent implements feedback by using the trained model to generate predictions and uncertainty estimates, which then inform the selection of the next batch of samples. This closed-loop process automatically determines the optimal number of samples to select based on the model's current performance and uncertainty, eliminating the need for manual intervention while maintaining training efficiency.
Solution Approach 2:
The system performs self-service by automatically selecting samples based on its own uncertainty estimates. The uncertainty estimator and sample selector work together to autonomously determine which samples are most informative and how many should be selected, removing the complexity of manual sample size determination while maintaining high training efficiency.
3Reliability
If more samples are selected for labeling, then model performance improvement increases, but resource expenditure increases
Solution Approach 1:
The patent applies partial action by selecting only the necessary number of samples that provide the most value for model improvement. Rather than labeling all available samples or using a fixed large number, the system selects a partial set of samples that maximizes performance improvement per unit of resource expenditure, as determined by the expected model variance and user-defined constraints.
Solution Approach 2:
The system changes the parameter of sample selection from a fixed or uniform approach to a dynamic approach based on expected model variance and resource constraints. By adjusting the number and type of samples selected based on these parameters, the system optimizes the trade-off between model performance improvement and resource expenditure.
Data Source
AI summary
Methods and systems are described herein for minimizing resource expenditure during model training using user-defined constraints in sample selection. A system may obtain user-defined target parameter values for data labeling, a user input indicative of a value added per unit of model performance improvement, and a dataset (e.g., unlabeled samples). The system may select a first subset of the dataset and may transmit a request for labeling the samples. The system may receive a first training dataset comprising label data and the samples of the first subset. The system may train a machine learning model using the first training dataset and generate a margin curve. Based on the margin curve, the system may determine whether an amount of resource usage exceeds value added and responsive to determining that it does not exceed the amount of resource usage, select a second subset of the dataset.