Dynamic Expert Gate for Long-Tail Categorization Bias
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Solution Overview
Problem
Machine learning models face challenges in predicting product categorization from textual descriptions due to long-tail data distribution bias, where head classes are over-represented, leading to decreased performance and increased human intervention, energy consumption, and errors.
Innovation Solution
The approach involves training multiple experts for different ranges of classes (head, body, tail) and dynamically combining them using a multilayer perceptron gate to optimize predictions, reducing bias and the need for human intervention by calculating weights for each expert based on sample characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine learning models are trained on imbalanced data with head classes over-represented, then training data processing is simplified, but prediction accuracy for tail classes deteriorates
Solution Approach 1:
The patent segments the classification task into multiple specialized models, each trained on a specific subset of classes (head, body, tail). This allows each model to focus on its specific domain without the noise from imbalanced data, thereby improving prediction accuracy for tail classes while maintaining ease of data processing through targeted training approaches.
Solution Approach 2:
The patent applies local quality by creating specialized classification models tailored to specific local regions of the class distribution. Each model is optimized for its specific subset (head, body, or tail classes), ensuring high prediction accuracy for tail classes without being compromised by the overall imbalanced dataset structure.
2Measurement precision
If multiple experts are trained for different class ranges and dynamically combined, then prediction accuracy improves, but device complexity increases
Solution Approach 1:
The patent employs a dynamic combination mechanism that selects and weights multiple expert models based on the input sample's characteristics. This dynamic approach allows the system to adaptively choose the most appropriate expert for each prediction task, improving accuracy while managing complexity through intelligent selection rather than static use of all models.
Solution Approach 2:
The patent introduces an intermediary mechanism (the dynamic combination layer) that mediates between multiple expert models and the final prediction output. This intermediary selectively aggregates predictions from relevant experts, improving overall accuracy while containing complexity by not directly connecting all experts to all inputs simultaneously.
3Measurement precision
If dynamic combination of experts is used with weight calculation, then prediction accuracy and reduced errors are achieved, but computational energy consumption increases
Solution Approach 1:
The patent applies partial action by activating only the necessary subset of expert models for each prediction task, rather than processing all models simultaneously. This selective activation reduces computational energy consumption while maintaining high prediction accuracy by focusing resources on the most relevant experts for each specific class range.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to train machine learning models to reduce categorization bias, the apparatus comprising: interface circuitry; machine readable instructions; and programmable circuitry to at least one of instantiate or execute the machine readable instructions to: calculate category information corresponding to samples based on a plurality of models; calculate task loss values associated with respective ones of the samples and respective ones of the plurality of models based on product category information; calculate gating loss values for a model gate based on category frequency information; and train the model gate based on a sum of the task loss and the gating loss, the training to derive weights corresponding to respective ones of the plurality of models.