Ensemble Prediction Weight Selection Under Distribution Shift
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Solution Overview
Problem
Existing ensemble prediction techniques suffer from decreased accuracy when the distribution of information related to the prediction target locally changes.
Innovation Solution
A prediction device and method that updates first and second weight vectors based on evaluation results, integrating prediction results using selected weight vectors to maintain accuracy in dynamically changing distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single weight vector is used for ensemble prediction, then the system is simple and easy to maintain, but the prediction accuracy decreases when the distribution of information locally changes
Solution Approach 1:
The patent segments the single weight vector into multiple first weight vectors, each associated with specific conditions. This allows the system to adapt to different distribution scenarios by selecting appropriate segmented weight vectors, resolving the contradiction between adaptability and simplicity.
Solution Approach 2:
The patent introduces dynamic weight vector selection and update mechanisms. Weight vectors are dynamically chosen based on current conditions and updated over time, enabling the system to adapt to changing information distributions while maintaining manageable complexity through structured dynamics.
2Measurement precision
If multiple weight vectors are maintained to handle distribution changes, then prediction accuracy improves, but the complexity of weight vector selection and management increases
Solution Approach 1:
The patent performs preliminary actions by pre-associating first weight vectors with specific conditions and pre-training multiple second weight vectors. This preparation work is done in advance, reducing the complexity of real-time weight vector selection while maintaining high prediction accuracy.
Solution Approach 2:
The patent introduces second weight vectors as intermediaries that manage the selection and integration of first weight vectors. This intermediary layer simplifies the complexity of directly managing multiple first weight vectors while preserving the ability to achieve high prediction accuracy through coordinated weight vector usage.
3Reliability
If weight vectors are frequently updated to adapt to changing distributions, then prediction accuracy is maintained, but the computational overhead and training time increase
Solution Approach 1:
The patent implements periodic weight vector updates rather than continuous updates. Second weight vectors are updated at regular intervals or triggered by significant distribution changes, maintaining prediction reliability while reducing the time loss associated with frequent updates.
Solution Approach 2:
The patent performs preliminary training of multiple second weight vectors in advance, preparing them for future use. This preliminary action reduces the need for extensive real-time training, thereby maintaining prediction reliability while minimizing the time loss during actual prediction operations.
Data Source
AI summary
A prediction device includes at least one memory storing instructions, and at least one processor configured to execute the instructions to update some or all of a plurality of first weight vectors and some or all of a plurality of second weight vectors based on an evaluation result obtained by evaluating performance of each of a plurality of models with reference to evaluation information including model input information for evaluation and a true value relevant to the model input information, and the evaluation information, and output an integrated prediction result obtained by integrating prediction results predicted by each model with reference to model input information included in prediction target information related to a prediction target using a weight vector selected based on the prediction target information from among the plurality of first weight vectors and the plurality of second weight vectors for decision making.