Unsupervised Feature Selection for Online Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models trained offline become outdated quickly, leading to inaccurate determinations and predictions, especially in dynamic environments like financial markets during events such as wars, earthquakes, or pandemics.
Innovation Solution
The method involves unsupervised feature selection for online machine learning, where multiple online data streams are processed to determine aggregate feature sets that can update models in real time, ensuring relevance and reducing memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If models are trained offline using stored data, then model training can be performed with complete data availability, but the models become outdated and provide inaccurate determinations and predictions in dynamic environments
Solution Approach 1:
The patent transitions from static offline model training to dynamic online model training that continuously adapts to changing data streams. The system updates models in real-time as new data arrives, making the modeling process dynamic rather than static, thereby preventing model obsolescence in changing environments.
Solution Approach 2:
The system performs preliminary feature selection and data processing on incoming streams before model training. By pre-processing and selecting relevant features in advance, the system enables rapid online model updates without requiring complete retraining, thus maintaining accuracy while reducing the time lag between data availability and model deployment.
2Loss of information
If all data from multiple data sources is used for training, then comprehensive information is available, but memory constraints are exceeded and processing becomes inefficient
Solution Approach 1:
The patent extracts and selects only the most relevant features from multiple data streams using unsupervised feature selection. Instead of processing all available data, the system identifies and extracts key features that contribute most to model performance, thereby reducing data volume while maintaining information quality and avoiding memory constraints.
Solution Approach 2:
The system applies different processing strategies to different data sources and features based on their relevance and quality. Rather than treating all data uniformly, the patent selectively processes high-value features in real-time while aggregating or filtering lower-priority data, optimizing the balance between data completeness and processing efficiency.
3Measurement precision
If feature selection is performed offline, then feature selection can be done with complete data analysis, but the selected features may not reflect current data patterns and online adaptation is lost
Solution Approach 1:
The patent implements continuous feature selection that operates ongoing as new data streams arrive, rather than performing feature selection once offline. This continuous process maintains feature relevance by constantly evaluating and selecting features based on current data patterns, thereby preserving both selection accuracy and online adaptability.
Solution Approach 2:
The system incorporates feedback loops where model performance on incoming data informs feature selection decisions. By continuously monitoring how well selected features perform in real-time and adjusting feature selection accordingly, the system maintains high feature selection accuracy while adapting to changing data distributions online.
Data Source
AI summary
Systems and methods for unsupervised feature selection for online machine learning are provided. Features can be selected from a plurality of online data sources having a plurality of respective online data streams, and an aggregated feature set and aggregated data can be formed therefrom. The aggregated feature set and the aggregated data can be used by machine learning models in real time to provide real time online machine learning.