Ordering Annotation Sets for Machine Learning Models
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Solution Overview
Problem
Current techniques for ordering data points in machine learning datasets do not effectively improve the learning process, often prioritizing human effort reduction over enhancing model efficiency.
Innovation Solution
A method that determines total weights for data points based on ordering constraints, generating an ordered annotation set to prioritize data points that address multiple weaknesses with fewer data points, thereby improving the efficiency of training machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data points are ordered based on traditional techniques focused on reducing human labeling effort, then human effort in labeling is reduced, but the actual machine learning training efficiency is not improved
Solution Approach 1:
The patent changes the ordering parameters from human-centric metrics to model-centric metrics. It calculates weights based on dataset weaknesses, hole filling potential, and model performance impact, fundamentally altering how data points are prioritized from a different parameter space that directly serves model training needs
Solution Approach 2:
The system incorporates feedback loops where the model's current performance and dataset weaknesses are continuously assessed. The ordering of data points is dynamically adjusted based on feedback about which data classes most need improvement, creating a closed-loop system that adapts to actual model needs rather than static ordering rules
2Reliability
If the annotation set includes all necessary data points to address dataset weaknesses, then the model learning completeness is improved, but the training time and computational resources increase
Solution Approach 1:
The patent extracts and prioritizes only the most critical data points needed to address specific dataset weaknesses. Rather than using all available data uniformly, it identifies and extracts the subset of data points that provide maximum value for filling holes in the dataset, thereby reducing training time while maintaining learning completeness
Solution Approach 2:
The system applies partial action by selectively processing and ordering only the necessary portion of data points based on calculated weights and weaknesses. It avoids the excessive action of uniformly processing all data points by focusing computational effort on the most impactful subset, achieving effective training with reduced time investment
3Adaptability or versatility
If data points are prioritized to fill multiple holes in the dataset, then the dataset coverage is improved, but the complexity of determining priorities increases
Solution Approach 1:
The patent creates a universal weighting system that evaluates data points across multiple dimensions simultaneously. The same weight calculation mechanism handles different data classes, weakness types, and priority criteria in a unified framework, making the system adaptable to various dataset configurations without requiring separate complexity for each consideration
Solution Approach 2:
The system merges multiple prioritization criteria into a single composite weight score for each data point. By combining hole-filling potential, data class importance, and model performance impact into one integrated metric, it simplifies the prioritization process while maintaining comprehensive dataset coverage across multiple dimensions
Data Source
AI summary
Embodiments are disclosed for a method. The method includes receiving an annotation set for a machine learning model. The annotation set includes multiple data points relevant to a task for the machine learning model. The method also includes determining total weights corresponding to the data points. The total weights are determined based on multiple ordering constraints indicating multiple data classes and corresponding weights. The corresponding weights represent a relative priority of the data classes with respect to each other. The method further includes generating an ordered annotation set from the annotation set. The ordered annotation set includes the data points in a sequence based on the determined total weights.