Ensemble Model Construction via Dynamic Sampling Distribution
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Solution Overview
Problem
Conventional boosting algorithms often rely on a single type of base learner and uniform sampling distribution, leading to biased gradient estimates, over-fitting, and limited convergence to local minima, resulting in suboptimal generalization accuracy.
Innovation Solution
A method that performs multiple boosting iterations with random selection of base learners based on a changing sampling probability distribution, altering it at least once during the process, to introduce stochasticity and reduce bias, allowing convergence to a wider range of local minima.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform sampling distribution is used for base learner selection, then the selection process is simple, but the gradient estimates become biased and generalization accuracy decreases
Solution Approach 1:
The patent changes the sampling probability distribution from uniform to non-uniform based on the performance characteristics of different base learners. Specifically, it assigns higher sampling probabilities to base learners that achieve lower training losses, thereby adjusting the parameter distribution to reduce gradient estimation bias while maintaining computational feasibility.
2Device complexity
If a single type of base learner is used, then the algorithm is simple to implement, but the model suffers from over-fitting and limited convergence
Solution Approach 1:
The patent employs an ensemble of diverse base learner types (e.g., decision trees, neural networks, SVMs) rather than a single type. This composite approach combines the strengths of different learner types to achieve better generalization and convergence properties while managing complexity through a unified boosting framework that handles multiple learner types.
Solution Approach 2:
The patent dynamically adjusts the sampling probability distribution during the boosting process based on observed performance metrics. The distribution evolves from uniform initially to a non-uniform distribution that favors better-performing base learner types as training progresses, enabling adaptive convergence behavior.
3Stability of the object's composition
If base learners are selected without altering sampling distribution, then the training process is consistent, but the model achieves suboptimal generalization accuracy
Solution Approach 1:
The patent implements periodic re-evaluation and adjustment of the sampling probability distribution during the boosting iterations. At specified intervals, the distribution is recalibrated based on accumulated performance data, introducing controlled variations that improve generalization while maintaining overall training stability through the additive boosting framework.
Data Source
AI summary
In an approach for constructing an ensemble model from a set of base learners, a processor performs a plurality of boosting iterations, where: at each boosting iteration of the plurality of boosting iterations, a base learner is selected at random from a set of base learners, according to a sampling probability distribution of the set of base learners, and trained according to a training dataset; and the sampling probability distribution is altered: (i) after selecting a first base learner at a first boosting iteration of the plurality of boosting iterations and (ii) prior to selecting a second base learner at a final boosting iteration of the plurality of boosting iterations. A processor constructs an ensemble model based on base learners selected and trained during the plurality of boosting iterations.