Machine Learning Predictive Labeling via Mutual Information
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Solution Overview
Problem
Traditional active learning methods are inefficient in querying and selecting samples for classification tasks, as they ignore the structure and interactions within data, making it difficult to label structured or hierarchically organized data, such as web pages or social media data, which requires significant human labor and is costly.
Innovation Solution
A semi-supervised learning approach that uses a computer-readable medium to execute instructions for predicting events or classifying objects by training a labeling model on labeled observation vectors, computing label distribution probabilities, and selecting observation vector combinations with high mutual information values to efficiently label unlabeled data, preserving the dependency and interaction between vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional active learning methods are used to query and select individual samples, then the sampling process is simple, but the labeling accuracy and classification performance deteriorate due to ignoring data structure and interactions
Solution Approach 1:
The patent merges multiple observation vectors into groups or clusters based on their relationships and interactions within the data structure. Instead of selecting individual samples independently, the system identifies and selects groups of related observations that collectively provide more informative training signals, thereby improving labeling accuracy while accounting for data structure.
Solution Approach 2:
The patent introduces an intermediary component that analyzes the structure and interactions within the data before selecting samples for labeling. This intermediary layer processes the relationships between observations and identifies which groups of data points should be selected together, mediating between the simple sampling approach and the complex structure-aware requirements.
2Productivity
If traditional active learning methods select isolated samples, then the selection process is fast, but the ability to capture interactions and structures in hierarchical data deteriorates
Solution Approach 1:
The patent combines multiple related observation vectors into groups for simultaneous selection and labeling. By merging observations that are structurally related or interact with each other, the system captures interaction information that would be lost if samples were selected individually, while still maintaining efficient batch processing.
Solution Approach 2:
The patent performs preliminary analysis of data structure and relationships before the actual sample selection process. By pre-identifying groups of related observations and their interactions, the system prepares the information needed to make informed selection decisions, reducing the computational burden during the labeling process while preserving interaction information.
3Measurement precision
If human labor is used to label structured data, then the labeling quality is high, but the cost and time consumption increase significantly
Solution Approach 1:
The patent enables the system to automatically identify and select groups of observations that would be most beneficial to label, using the inherent structure and interactions within the data itself. This self-service approach reduces reliance on human judgment for sample selection while maintaining high labeling quality by focusing computational resources on the most informative data groups.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously evaluates the performance and information gain from labeled data groups. By monitoring which groups provide the most valuable training signals, the system can adaptively adjust its selection criteria to maintain high labeling quality while minimizing time consumption through automated iterative improvement.
Data Source
AI summary
A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix used to select a label for each observation. Unique combinations of observations selected from the unlabeled observations are defined. A marginal distribution value is computed from the label distribution probability matrix. A joint distribution value is computed between observations included in each combination. A mutual information value is computed for each combination as a combination of the marginal distribution value and the joint distribution value computed for the respective combination. A predefined number of observation vector combinations is selected from the combinations that have highest values for the computed mutual information value. Labeled observation vectors are updated to include each observation vector included in the selected observation vector combinations with a respective obtained label.