Classification Model Training with Distance-Aware Gradient Loss
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Solution Overview
Problem
Existing classification model training methods, such as Xgboost, face inaccuracies and slow training speeds due to uniform punishment for all classification errors, which hinders rapid improvement in classification accuracy.
Innovation Solution
Introducing a distance factor in the gradient loss function of the initial classification model to differentiate residuals based on the distance between true and predicted classifications, allowing for specific modification of the model according to varying error degrees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform punishment cost is applied to all classification errors, then the training process is simple, but the classification accuracy improvement is slow
Solution Approach 1:
The patent applies local quality by differentiating the punishment cost based on the local characteristic of each classification error - specifically the distance between predicted and true categories. Errors with larger distances receive higher punishment costs, while smaller distance errors receive lower costs. This localized differentiation accelerates convergence by focusing correction effort on more severe misclassifications.
Solution Approach 2:
The patent changes the parameter of punishment cost from a fixed uniform value to a dynamic value that varies with the distance parameter. By introducing the distance factor into the cost function, the punishment cost becomes a variable parameter that adapts to the severity of each classification error, thereby improving training efficiency and accuracy.
2Measurement precision
If distance factor is introduced in gradient loss function, then classification accuracy improves quickly, but the model complexity increases
Solution Approach 1:
The patent modifies the loss function by incorporating a distance factor parameter that quantifies the separation between predicted and true categories. This parameter enhancement transforms the gradient computation to be more informative about error severity, improving accuracy without requiring fundamental architectural changes to the model.
Solution Approach 2:
The distance factor serves as an intermediary element that bridges the prediction output and the loss computation. Rather than directly modifying the model architecture, the distance factor mediates between the predicted category and true category, providing a nuanced error signal that guides learning more effectively.
Data Source
AI summary
This application discloses a method and an apparatus for training a classification model. The method includes obtaining a training sample, the training sample including a training parameter and a true classification corresponding to the training parameter and preforming classification training on an initial classification model by using the training parameter, to obtain a predicted classification. The method also includes determining a residual between the true classification and the predicted classification according to a gradient loss function of the initial classification model, the gradient loss function comprising a distance factor representing a distance between a first category and a second category, the first category being a category to which the predicted classification belongs, and the second category being a category to which the true classification belongs. The method further includes modifying the initial classification model according to the residual to obtain a final classification model.