Asynchronous Bayesian Network Learning via Dual Pipeline Segmentation
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Solution Overview
Problem
Current Bayesian network models face challenges in balancing learning speed and accuracy, often requiring extensive time for more accurate models, which can lead to less accurate results over time, and rely on domain knowledge that may introduce bias or use ontologies with incorrect assumptions, posing a trade-off between model learning time and accuracy.
Innovation Solution
The implementation of a two-pipeline system for asynchronous learning, where one pipeline builds temporary models using ontologies for fast results and another pipeline builds a more accurate model using a full dataset, allowing for concurrent model building and progressive improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Bayesian network model uses more time for learning, then the model accuracy is improved, but the learning time increases substantially
Solution Approach 1:
The patent divides the model learning process into two separate pipelines: a fast pipeline that uses ontologies and domain knowledge to build models quickly, and a slow pipeline that uses full free learning with the complete dataset for high accuracy. This segmentation allows the system to provide both fast approximate models and accurate models simultaneously without forcing a trade-off between speed and precision.
Solution Approach 2:
The fast pipeline applies partial action by using only portions of the data (subsets) and incorporating pre-existing ontologies and domain knowledge to build models quickly. This partial approach sacrifices some completeness for speed, while the slow pipeline uses excessive action by processing the full dataset without constraints to achieve maximum accuracy.
2Productivity
If a Bayesian network model uses domain knowledge to speed up learning, then the learning time is reduced, but bias may be introduced
Solution Approach 1:
The patent separates the modeling process into two independent pipelines: the fast pipeline that incorporates ontologies and domain knowledge (which may introduce bias) and the slow pipeline that uses free learning without such constraints. By segmenting the process, the system can provide both biased-but-fast models and unbiased-but-slow models, allowing users to choose based on their specific needs.
Solution Approach 2:
The patent introduces an intermediary mechanism where the slow pipeline's accurate models can feedback into and improve the fast pipeline's models over time. This feedback loop acts as a mediator that allows the fast pipeline to gradually incorporate corrections from the more thorough slow pipeline, reducing bias while maintaining speed advantages.
Data Source
AI summary
A method, a computer system, and a computer program product for creating multiple models asynchronously is provided. Embodiments of the present invention may include receiving input data, wherein input data includes a full training dataset. Embodiments of the present invention may include building, asynchronously, one or more Bayesian network models using one or more portions of the input data on a first pipeline and building a free learning model using the full training dataset on a second pipeline. Embodiments of the present invention may include retrieving the one or more Bayesian network models from the first pipeline. Embodiments of the present invention may include retrieving the free learning model from the second pipeline.