Faceted Model Training for Context-Specific Classification Accuracy
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Solution Overview
Problem
Existing machine learning models trained on a single dataset may not perform optimally when applied to data sources with different contexts, leading to suboptimal or surprising results due to variations in user tolerance and domain-specific language usage.
Innovation Solution
Training machine learning models using a hybrid dataset created by selecting records from a data source based on faceting strategies such as time, channel, subject matter, or person, enriching the model with non-overlapping records from the same data source to enhance its applicability across diverse data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single machine learning model is trained on a general dataset and applied to all data sources, then the model can be universally applied across multiple sources, but the classification accuracy deteriorates when the data source context differs from the training data
Solution Approach 1:
The patent segments the training data into multiple faceted datasets, where each facet represents a specific context or domain (e.g., different time periods, channels, subject matters, or persons). Instead of using a single general dataset, the system creates specialized training subsets that capture different contextual variations, allowing the model to learn context-specific patterns while maintaining universal applicability through multiple facets.
Solution Approach 2:
The patent applies local quality by training the model on faceted datasets where each facet is optimized for its specific context. For example, records are selected based on time-based faceting to capture temporal variations in language usage, or channel-based faceting to account for differences across communication platforms. This ensures that each training subset has the appropriate local characteristics for its domain.
2Quantity of substance
If the training dataset includes all records from a data source, then the model has comprehensive training data, but the model cannot distinguish between legitimate and threatening uses of certain words in the same context
Solution Approach 1:
The patent extracts specific subsets of records from the complete data source based on faceting criteria. Instead of using all records, the system selectively extracts records that match specific facets (e.g., records from particular time windows, channels, or subject matters). This extraction process creates training datasets that are tailored to specific contextual scenarios, enabling the model to learn nuanced distinctions between legitimate and threatening language uses.
Solution Approach 2:
The patent performs preliminary faceting and record selection before model training. By pre-processing the data to create faceted datasets with specific contextual characteristics, the system prepares targeted training subsets that are optimized for learning specific patterns. This preliminary action ensures that the model receives context-appropriate training data before being applied to classification tasks.
Data Source
AI summary
A system and method for training machine learning models using generated faceted models. A faceted model is trained using a hybrid dataset, which is defined by selecting records from a data source according to a faceting strategy. For example, a user may train a faceted model to be applied to records from a data source in a defined time window. The hybrid dataset can be defined by a time based faceting strategy, where records are selected for the hybrid dataset from the same data source, but outside of the time window. The faceted model is enriched by records from the same data source that are not overlapping with the facets that define the subset of the data source where the model will be applied.