Knowledge Representation for Automated Classifier Training Data
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Solution Overview
Problem
The challenge of determining computationally efficient methods to identify information of interest from vast digital content sets without overwhelming users with irrelevant data, and efficiently training machine-learning classifiers with limited labeled training data.
Innovation Solution
A method and system for generating training data using a knowledge representation based on an object of interest, synthesizing concepts and relationships, and applying machine-learning classifiers to label and refine content items, leveraging attributes derived from the knowledge representation to assign relevance scores and labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling methods are used to create training data, then labeling accuracy can be ensured, but the time consumption and labor costs increase significantly
Solution Approach 1:
The system enables self-service by using the machine learning classifier to automatically label content items without human intervention. The classifier is trained on a small set of manually labeled data and then autonomously labels the remaining content items, eliminating the need for continuous manual labeling while maintaining acceptable accuracy through iterative refinement.
Solution Approach 2:
A small portion of content items (e.g., 10-20%) are manually labeled in advance to create initial training data. This preliminary action enables the machine learning classifier to be trained and deployed, significantly reducing the need for subsequent manual labeling while maintaining labeling quality.
2Quantity of substance
If a large volume of content items are processed, then comprehensive information coverage is achieved, but computational complexity and processing time increase
Solution Approach 1:
The content processing system is segmented into distinct functional modules: a training data generation module that creates labeled training data, a model training module that trains the classifier, and a content classification module that applies the trained classifier. This segmentation allows each module to be optimized independently and processed in parallel, reducing overall computational complexity.
Solution Approach 2:
The system performs preliminary action by pre-processing content items to extract features and pre-training the classifier on a subset of data. This preliminary processing reduces the computational burden during actual classification by pre-computing features and having the model ready for rapid inference on the full content volume.
3Reliability
If more labeled training data is used, then classifier accuracy improves, but the cost and time of data preparation increase
Solution Approach 1:
The system applies partial action by using only a small portion of content items (e.g., 10-20%) for manual labeling to create training data. This partial labeling is sufficient to train an effective classifier, avoiding the need to manually label the entire content set while achieving acceptable accuracy through the classifier's ability to generalize from limited examples.
Solution Approach 2:
The system implements feedback by using the classifier's performance on validation data to iteratively improve the model. Misclassified items can be added to the training set for retraining, and the system continuously refines the classifier accuracy through feedback loops without requiring proportional increases in manual labeling effort.
Data Source
AI summary
Systems and methods are provided for generating training data for a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used to assign labels to content items. The labeled content items can be used as training data for training a machine learning classifier. The labeled content items can also be used as validation data for the classifier.