Online Machine Learning With Expert Feedback for Stable Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning systems face challenges in maintaining stability and robustness in dynamic environments, such as finance, telecommunications, and health, where data continuously changes, and there is a need for models that can learn continuously and be understandable by human experts.
Innovation Solution
A continuously learning, stable, and robust online machine learning system (OMLS) that learns concurrently from both streaming data and human-provided feedback, incorporating an Online Machine Learning Engine (OMLE), Online Preprocessing Engine (OPrE), Online Feature Engineering Engine (OFEE), and Online Robust Feature Selection Engine (ORFSE), along with an Online Explanation System (OES) and Online Human Expert Feedback System (OEFS) to provide transparent and updatable models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained in batch mode on available datasets, then model performance can be optimized on training data, but the models fail to adapt to continuously changing data patterns and require periodic retraining
Solution Approach 1:
The patent implements dynamic online learning where model parameters are continuously updated as new data arrives, transforming the static batch training approach into a dynamic adaptive system. The model evolves over time to track changing data distributions while maintaining performance through incremental parameter adjustments.
Solution Approach 2:
The system incorporates feedback mechanisms where model predictions and actual outcomes are continuously monitored, and performance metrics are used to adjust learning rates and parameters. This feedback loop enables the model to self-correct and adapt to changing patterns in the data stream.
2Measurement precision
If complex machine learning models are used to capture emerging patterns, then detection accuracy improves, but model stability decreases and overfitting occurs
Solution Approach 1:
The patent dynamically adjusts model parameters including learning rates, regularization strengths, and complexity controls based on data characteristics and performance metrics. This parameter adaptation allows the model to balance complexity and stability, preventing overfitting while maintaining detection accuracy.
Solution Approach 2:
The system uses ensemble methods and incremental learning where multiple simpler models are combined, or where learning is performed partially on new data while retaining knowledge from previous data. This approach achieves accurate pattern detection without the instability of overly complex single models.
3Reliability
If models are periodically retrained to maintain performance, then model accuracy can be preserved, but business continuity is disrupted and time is lost
Solution Approach 1:
The patent implements continuous online learning where the model learns incrementally from incoming data streams without interruption. This eliminates the need for periodic batch retraining, maintaining both model accuracy and business continuity simultaneously through uninterrupted learning operations.
Solution Approach 2:
The system performs preliminary incremental learning updates on new data before full model deployment, allowing the model to adapt gradually to changing patterns while maintaining stable predictions during transitions, thus preserving business continuity.
4Adaptability or versatility
If machine learning models continuously change to adapt to new data, then adaptability improves, but model understandability and accountability decrease
Solution Approach 1:
The patent introduces explanation systems and monitoring intermediaries that translate complex model decisions into understandable formats for human operators. These intermediaries maintain model adaptability while providing interpretable insights into model behavior, preserving accountability and understandability.
Solution Approach 2:
The system segments the model into interpretable components and tracks changes in each component separately. This segmentation allows continuous adaptation while maintaining understandability by breaking down complex transformations into traceable, explainable steps that can be monitored and understood by human operators.
Data Source
AI summary
An Online Machine Learning System (OMLS) includes an Online Machine Learning Engine (OMLE) for incorporating and utilizing one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms; wherein the OMLS is configured to perform continuous online machine learning, the continuous online machine learning comprising: continuous online machine learning from streaming data including an instance comprising a vector of inputs, the vector of inputs comprising a plurality of continuous or categorical features; and continuous online machine learning from periodically provided expert feedback.