Dynamic Outlier Bias Reduction in Machine Learning Models
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Solution Overview
Problem
Machine learning models often suffer from bias due to outliers in training data, which can propagate and affect prediction accuracy, and existing methods fail to effectively address this issue.
Innovation Solution
The implementation of dynamic outlier bias reduction methods, where a processor iteratively refines model parameters by identifying and removing outliers based on error sets and bias criteria, generating a non-outlier data set, and training an outlier classifier to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained on complete training data sets including outliers, then the model can learn from all available data, but bias propagates into predictions reducing accuracy
Solution Approach 1:
The system performs preliminary identification and removal of outliers from the training data set before final model training. By pre-processing the data to eliminate biased samples, the model is trained on cleaner data, preventing bias propagation into predictions while maintaining reliability.
Solution Approach 2:
The system extracts and removes outlier data points from the training data set based on calculated outlier scores. By separating harmful outlier samples from the legitimate training data, the model learns from non-outlier data only, eliminating the harmful bias while preserving useful information from the majority of data.
2Measurement precision
If iterative refinement processes are used to remove outliers and improve accuracy, then prediction accuracy improves, but computational time and complexity increase
Solution Approach 1:
The system performs a limited number of iterative refinement passes rather than exhaustive processing. By applying outlier removal and model retraining for a predetermined number of iterations or until convergence criteria are met, the system achieves sufficient accuracy improvement without excessive computational time investment.
Solution Approach 2:
The system uses feedback from model performance metrics to guide the iterative refinement process. By monitoring accuracy improvements and convergence patterns, the system can terminate iterations early when sufficient accuracy is achieved, balancing precision improvement with time efficiency.
Data Source
AI summary
Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity.