Kernel Gradient Boosting Model for Faster Convergence and Uncertainty Estimation
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Solution Overview
Problem
Conventional decision-tree based Machine Learning Algorithms, particularly those using Gradient Boosting and Stochastic Gradient Langevin Boosting, face challenges such as slow convergence and high certainty predictions when encountering out-of-domain data, especially with non-convex loss functions.
Innovation Solution
The proposed Kernel Gradient Boosting (KGB) model generates a combination of first and second trees, where the first trees have randomly determined structures with non-null leaf values and the second trees are generated using a Gradient Boosting algorithm, enabling faster convergence and better uncertainty estimation by assigning leaf values based on noise injection and gradient values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Gradient Boosting or Stochastic Gradient Langevin Boosting is used to train decision-tree based MLA, then the model can handle complex prediction tasks, but the convergence speed is slow and the model shows high certainty when encountering out-of-domain data
Solution Approach 1:
The training process is segmented into two distinct phases: (1) generating a first tree with randomly determined structure and non-null leaf values using noise injection, and (2) generating a second tree using traditional Gradient Boosting algorithm. This segmentation allows each phase to address different aspects of the problem - the first tree provides robust uncertainty estimation while the second tree ensures fast convergence on in-domain data.
Solution Approach 2:
The first tree is generated in advance using noise-inducing functions before the main Gradient Boosting training. This preliminary action establishes a foundation of uncertainty awareness that prevents the model from making overly confident predictions on out-of-domain data, while the subsequent second tree refinement maintains convergence speed.
2Productivity
If traditional Gradient Boosting is used, then the model converges on training data, but it provides high certainty predictions for out-of-domain data which reduces reliability
Solution Approach 1:
Different qualities are assigned to different parts of the model: the first tree has randomly determined structures with non-null leaf values generated through noise injection, providing local uncertainty estimation capability, while the second tree uses traditional Gradient Boosting for efficient convergence on training data. This local quality differentiation enables the model to maintain both training efficiency and out-of-domain detection reliability.
Solution Approach 2:
The model combines two不同类型 of trees into a composite structure - a first tree with noise-inducing random structure and a second tree with gradient-optimized structure. This composite approach integrates the uncertainty estimation capability of random trees with the convergence efficiency of gradient boosting, achieving both productivity and reliability.
3Reliability
If noise injection is used to generate non-null leaf values in first trees, then uncertainty estimation improves, but the model complexity increases
Solution Approach 1:
Noise injection is applied partially - only to the first tree generation phase, not to the entire model or all training iterations. This partial application provides sufficient uncertainty estimation capability without unnecessarily complicating the overall model structure or training process.
Data Source
AI summary
Methods and processors for training a decision-tree based Machine Learning Algorithm (MLA) are disclosed. During a first training iteration, the processor generates a first tree, which includes generating a first tree structure with leaf nodes. The at least one from the plurality of training objects falling in one leaf node and none falling in an other node. The leaf values being based on a first noise-inducing function such that they are non-null leaf values. During a second training iteration of the decision-tree based MLA, the processor generates a second tree with a second tree structure with a third leaf node. A third leaf value is based on an estimated gradient value of a loss function for at least one training object falling in the third leaf node. The processor is configured to store the first and the second tree of the decision-tree based MLA in a storage.