Ordinal Classification via Encoder Freezing and Segmented Binary Training
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Solution Overview
Problem
Existing ordinal classification methods face inefficiencies due to imbalanced classes leading to long training times and overfitting, especially when using shared neural network representations for binary classifiers.
Innovation Solution
Learning compact neural representations using an encoder neural network and freezing them for downstream tasks, allowing K−1 ordinal classifiers to be trained independently, thereby reducing training time and improving classification performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If shared neural network representations are used for training K-1 binary classifiers, then training efficiency is improved, but class imbalance leads to long training times and overfitting
Solution Approach 1:
The patent segments the classification problem into K-1 independent binary classifiers, each handling a specific threshold comparison. This segmentation allows each classifier to be trained independently on balanced datasets, avoiding the class imbalance problem that plagues joint training approaches while maintaining the efficiency benefits of shared representations.
Solution Approach 2:
The patent changes the training parameters by using synthetic data generation to create balanced class distributions for each binary classifier. By transforming the inherently imbalanced ordinal classification problem into multiple balanced binary problems through parameter manipulation (threshold selection and synthetic sampling), the training time is reduced while maintaining accuracy.
2Device complexity
If shared neural network representations are used for training K-1 binary classifiers, then model complexity is reduced, but some classifiers overfit while others are still training
Solution Approach 1:
The patent segments the training process into independent binary classifier training tasks, each with its own balanced dataset. This segmentation prevents overfitting in early-trained classifiers from affecting later classifiers, as each operates independently with appropriate regularization and data balancing specific to its threshold.
Solution Approach 2:
The patent applies partial training actions by training each binary classifier to the appropriate level of convergence based on its specific characteristics. Rather than training all classifiers uniformly, each classifier receives tailored training duration and intensity, preventing overfitting while ensuring sufficient learning for each threshold comparison.
Data Source
AI summary
A computer-implemented method for ordinal classification of input data is provided. The method includes learning, by an encoder neural network, compact neural representations of the input data. The method further includes freezing the encoder neural network for downstream tasks. The method also includes training, by a hardware processor, K−1 ordinal classifiers on top of the compact neural representations to obtained trained K−1 ordinal classifiers. The method additionally includes generating, by the hardware processor, a predicted ordinal label by aggregating the trained K−1 ordinal classifiers.