Sample Data Recommendation via Classification Boundary Distance
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Solution Overview
Problem
Manual labeling of sample data for machine learning is labor-intensive and often ineffective, with most labeled data not significantly impacting classification effects, particularly at the classifying boundary where data quality is crucial.
Innovation Solution
A method and apparatus for recommending sample data by inputting unlabeled data into pre-trained classification models to acquire classifying probabilities, calculating distances to the classifying boundary, and selecting target data based on these distances to optimize labeling efforts, reducing manual labeling costs and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling of sample data is performed to improve classification effect, then classification effectiveness is improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The system enables self-service by allowing the classification model to automatically identify and select its own boundary samples based on probability distances, eliminating the need for manual selection and labeling of all training samples. The model serves itself by autonomously determining which samples are most critical for boundary refinement.
Solution Approach 2:
Instead of manually labeling all sample data (excessive action), the system applies partial action by selectively labeling only the critical boundary samples identified through probability distance calculation. This partial approach focuses labeling efforts on the most impactful subset of data, significantly reducing overall labeling workload while maintaining classification effectiveness.
2Reliability
If all sample data is manually labeled to ensure data quality, then data quality is improved, but labeling cost increases
Solution Approach 1:
The system applies local quality by differentiating the importance of different samples based on their proximity to classification boundaries. Boundary samples receive high-quality manual labeling due to their critical importance, while non-boundary samples rely on automated model predictions. This localized quality allocation ensures data quality where it matters most while reducing overall labeling costs.
Solution Approach 2:
The classification model performs self-service by automatically labeling non-boundary samples based on its probability predictions, eliminating the need for expensive manual labeling of these samples. The model leverages its own learned patterns to confidently classify samples that are clearly away from decision boundaries, reserving human expertise only for ambiguous boundary cases.
3Measurement precision
If boundary samples are manually labeled to improve classification accuracy, then classification accuracy is improved, but the complexity of organizing labeling training increases
Solution Approach 1:
The system extracts the critical function of boundary sample identification from the complex process of manual data curation. By calculating probability distances and automatically identifying boundary samples, the system separates the identification task from the labeling task, reducing the organizational complexity of coordinating expert reviewers while maintaining the ability to label boundary samples with high precision.
4Reliability
If cross validation is performed to ensure labeled data quality, then data quality assurance is improved, but time consumption and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-identifying boundary samples through probability distance calculation before the labeling process begins. This preliminary identification ensures that only critical samples require manual labeling and subsequent validation, reducing the scope and time cost of cross-validation efforts while maintaining quality assurance for the most important labeled data.
Data Source
AI summary
The present disclosure proposes a method and an apparatus for recommending sample data. The method may include: inputting a plurality of pieces of sample data to be classified into at least one preset classification model, and acquiring a classifying probability of classifying each piece of sample data into each classification model; acquiring a first distance between each piece of sample data and a classifying boundary of each classification model according to the classifying probability of classifying the piece of sample data into the classification model, in which the classifying boundary of the classification model is configured to distinguish positive and negative sample data; computing a target distance for each piece of sample data according to the first distance between each piece of sample data and the classifying boundary of each classification model.