Ensemble Confidence Models for Ranked Classification Accuracy
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Solution Overview
Problem
Existing machine learning models (MLMs) often produce classification results that are not accurate enough for specific tasks, necessitating improved methods to enhance prediction accuracy.
Innovation Solution
Training an ensemble confidence machine learning model (EC-MLM) that combines multiple predictor MLMs and confidence MLMs to generate an aggregated ranked list of classifications, using weighted predictions and confidences to select the most likely correct classification for each data item.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning model is used for classification, then the system complexity is low, but the classification accuracy is insufficient
Solution Approach 1:
The system segments the classification task into multiple independent predictor models, each trained on different aspects or subsets of the training data. These segmented models work in parallel to provide diverse predictions, which are then aggregated to achieve higher overall accuracy than any single model could achieve alone.
Solution Approach 2:
The system merges the predictions from multiple predictor models and confidence models into a unified ensemble confidence model. By combining the outputs of these individual models through aggregation and ranking mechanisms, the system achieves superior classification accuracy that exceeds the performance of individual models.
2Measurement precision
If multiple predictor models are trained and combined, then the classification accuracy improves, but the training time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by training multiple predictor models and confidence models in advance during the training phase. These pre-trained models are then ready for rapid inference during deployment, where the ensemble confidence model can quickly aggregate their predictions without requiring additional real-time training computations.
Solution Approach 2:
The confidence models serve as intermediaries that evaluate and weight the predictions from multiple predictor models. This intermediary layer enables the system to efficiently combine multiple predictions by using confidence scores to prioritize reliable predictions, reducing the computational burden of processing all predictions equally.
3Reliability
If confidence models are trained to evaluate predictor model outputs, then the reliability of classifications improves, but the device complexity increases
Solution Approach 1:
The confidence models serve multiple functions: they evaluate the reliability of predictor model predictions, provide weighting factors for aggregation, and contribute to the overall ensemble decision-making process. This multi-functionality allows the system to improve reliability without adding separate dedicated components for each function.
Solution Approach 2:
The confidence models provide feedback mechanisms by evaluating the quality of predictions from predictor models and adjusting the aggregation process accordingly. This feedback loop enables the ensemble confidence model to dynamically weight predictions based on their reliability, improving overall classification trustworthiness while maintaining a manageable architecture through iterative refinement.
Data Source
AI summary
A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.