Classification Model Loss Function Modification for Imbalanced Data
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Solution Overview
Problem
Existing data classification models struggle with imbalanced data sets, where one class significantly outnumber the other, leading to biased classifications and inefficiencies in identifying minority classes, common in fraud detection and other real-world applications.
Innovation Solution
A system that modifies the loss function to penalize false positive misclassifications more than false negative misclassifications, allowing for the generation of a classification model that mitigates bias and minimizes quantifiable loss, using a computer-implemented method to update and reclassify data based on negative impact analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification models are used on imbalanced data sets, then the model training is simple and fast, but the classification accuracy for minority classes deteriorates and bias toward majority classes increases
Solution Approach 1:
The patent modifies the loss function parameters by introducing class-specific weighting factors and focal modulation terms that dynamically adjust the contribution of different classes during training. This transforms the standard loss function into a customized version that explicitly accounts for class imbalance, allowing the model to prioritize minority class detection without requiring complex architectural changes or data resampling techniques
Solution Approach 2:
The patent applies preliminary data transformation techniques including synthetic minority class oversampling and feature space normalization before model training. These preprocessing steps balance the class distribution and standardize input features, enabling the subsequent modified loss function to operate more effectively and reducing the overall model complexity needed to achieve balanced classification performance
2Reliability
If classification models prioritize majority class accuracy, then overall model performance metrics improve, but minority class detection capability deteriorates
Solution Approach 1:
The patent introduces asymmetric weighting in the loss function where minority class misclassifications are penalized more heavily than majority class misclassifications. This asymmetric treatment creates a balanced objective function that simultaneously optimizes for both majority class accuracy and minority class detection, preventing the model from becoming biased toward the majority class while maintaining overall performance
Solution Approach 2:
The patent implements a feedback mechanism through the modified loss function that continuously monitors classification performance across different classes and adjusts the weighting factors accordingly. This dynamic feedback loop ensures that the model maintains balanced attention to both majority and minority classes throughout training, preventing performance degradation in either class while optimizing overall reliability
3Productivity
If standard loss functions are used for training, then the training process is computationally efficient, but classification bias toward majority classes increases
Solution Approach 1:
The patent modifies the loss function parameters by incorporating class frequency-based weighting factors and focal modulation terms that directly address classification bias. These parameter changes are integrated into the existing training framework, allowing the model to correct bias toward majority classes while maintaining computational efficiency and avoiding the need for retraining from scratch or using computationally intensive alternative methods
Data Source
AI summary
A data classification system obtains a data set including first data associated with a first characteristic and second data associated with a second characteristic. In response to obtaining the data set, the system uses a classification model to generate a classification by classifying the first data into majority data and the second data into minority data. From the classification and using the classification model, the system determines a loss. The system modifies the classification model based on this loss to generate an updated classification model. The system uses the updated classification model to re-classify a subset of the first data into the minority data and a subset of the second data into the majority data.