Adaptive Classifier Switching for Continuous Learning Under Concept Drift
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Solution Overview
Problem
Conventional deep learning networks face challenges in adapting to dynamic environments with significant concept drift, leading to catastrophic forgetting of previously learned knowledge due to architectural complexity and resource constraints, making them unsuitable for scalable real-time analytics.
Innovation Solution
A system that dynamically switches between less complex ML classifiers trained without continuous learning and more complex classifiers trained with continuous learning based on data drift, using adaptive clustering to train new classifiers when necessary, thereby mitigating catastrophic forgetting while reducing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning networks are retrained using newer windows of streaming data to continually generate new learnings, then the model can adapt to concept drift, but catastrophic forgetting occurs where previously learned knowledge is lost
Solution Approach 1:
The patent segments the training process into distinct phases: initial training on historical data, then continuous learning phases on streaming data windows. Each phase maintains separate learnings that are integrated through regularization techniques, preventing catastrophic forgetting while enabling adaptation to concept drift.
Solution Approach 2:
The patent modifies network parameters (learning rates, regularization weights, temperature coefficients) dynamically during continuous learning to balance between retaining old knowledge and acquiring new knowledge. These parameter changes control the trade-off between adaptability and knowledge retention.
2Measurement precision
If deep learning networks are periodically retrained using historical data and rapidly increasing volume of streaming data, then model accuracy improves, but the process quickly becomes infeasible due to computational complexity
Solution Approach 1:
The patent extracts only the most recent window of streaming data for continuous learning instead of retraining on all historical data plus new data. This extraction approach maintains model accuracy while dramatically reducing computational complexity by processing only essential new information.
Solution Approach 2:
The patent performs preliminary training on historical data before deployment, then uses this pre-trained model as the foundation for continuous learning on streaming data. This preliminary action eliminates the need for periodic retraining on all historical data, reducing computational complexity while maintaining accuracy.
3Adaptability or versatility
If deep learning networks unlearn some prior representations to replace them with new representations of current training data, then the model can learn new patterns, but previously learned knowledge is lost leading to catastrophic forgetting
Solution Approach 1:
The patent applies preliminary anti-action by introducing regularization terms (EWC, L2) before the network can forget old knowledge. These regularization constraints prevent the network from unlearning prior representations by penalizing deviations from previously learned parameter values, thus counteracting the natural tendency toward catastrophic forgetting.
Solution Approach 2:
The patent implements feedback mechanisms where performance on both old and new tasks is monitored during continuous learning. This feedback guides the regularization strength and learning rate adjustments to maintain performance on previously learned patterns while acquiring new knowledge from current data streams.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support adaptive machine learning (ML) classification that mitigates effects of catastrophic forgetting while reducing overall resource requirements. To illustrate, a computing device may train first and second ML classifiers based on historical streamed data. The second ML classifier is trained to use continuous learning, and the first ML classifier is not. If data drift of a data stream is below a lower threshold, the data stream is provided as input to the first ML classifier to generate classification output (e.g., predictions). If the data drift is above the lower threshold, dynamic switching occurs and the data stream is provided as input to the second ML classifier instead of the first ML classifier to generate the classifier output. If the data drift is above an upper threshold, operations are performed to train new ML classifiers.