Stationary Classification Model Training via Un-stationary Feature Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models face challenges due to unstable features, which can lead to decreased predictive power as feature value distributions change over time.
Innovation Solution
A method and system for dynamically detecting un-stationary features by comparing feature value distributions in training data and unlabeled data, and adjusting the training data to minimize the impact of these features, thereby training a stationary classification model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is continually collected and model is completely re-trained regularly, then the model reflects current situation, but the cost increases due to regular re-training
Solution Approach 1:
The system dynamically adjusts the training process by detecting un-stationary features and selectively re-training only the affected portions of the model rather than performing complete re-training. This dynamic adaptation allows the model to respond to changing data distributions while minimizing computational resources spent on unnecessary re-training operations.
Solution Approach 2:
The system changes the training parameters by identifying which features are un-stationary and adjusting the training strategy accordingly. Instead of re-training the entire model, the system modifies only the relevant parameters and weights associated with un-stationary features, thereby reducing the overall training cost while maintaining model accuracy.
2Reliability
If time series analysis is performed with respect to each feature, then feature stability is assessed, but data must be collected from a long period which increases complexity
Solution Approach 1:
The system extracts and focuses only on the critical aspect of feature stability detection without requiring comprehensive long-term time series analysis for all features. By identifying and isolating un-stationary features through statistical tests on available data, the system can assess feature stability without needing to collect and analyze every feature over extended periods, thereby reducing data collection complexity.
Solution Approach 2:
Instead of performing complete time series analysis on all features (excessive action), the system applies partial analysis by focusing only on detecting un-stationary features using statistical tests on the available training and unlabeled data. This partial action approach provides sufficient stability assessment without the complexity of comprehensive long-term data collection.
3Reliability
If un-stationary features are detected and training data is adjusted, then stable model is trained, but the process becomes more complex
Solution Approach 1:
The system segments the training process into distinct phases: detecting un-stationary features using statistical tests, identifying affected features, and selectively adjusting training data and model parameters. This segmentation allows the complex task of training a stable model to be broken down into manageable steps, reducing the overall complexity by addressing each phase systematically rather than attempting to handle all complexities simultaneously.
Data Source
AI summary
The present teaching relates to learning a model. Supervised training data with samples having feature values and a label is received. Unlabeled data be classified is received having samples with values of the same features. Un-stationary features in the supervised training data are detected based on respective feature values from the supervised training data and the unlabeled data. If un-stationary feature exists, adjusted training data set is created based on the supervised training data and the un-stationary features and used to train a stationary classification model. Otherwise, the supervised training data is used to train the stationary classification model.