Ensemble Learning System for AI Classification Error Correction
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Solution Overview
Problem
Machine learning classification systems face challenges in precision and recall due to erroneous and failed identifications, as they are prone to misclassifying elements and failing to classify certain data items, leading to suboptimal performance in tasks like voice recognition and image classification.
Innovation Solution
The implementation of an ensemble learning system that utilizes multiple trained machine-learning classifiers to reclassify misclassified elements and classify unclassified elements, with an ensemble classifier combining the outputs of these classifiers to improve precision and recall through iterative or recursive processes, including the use of additional data augmentation and classification by separate classifiers for misclassified and unclassified data items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single machine-learning classifier is used to classify elements in data items, then the system is simple and fast, but the precision and recall are suboptimal due to erroneous and failed identifications
Solution Approach 1:
The patent combines multiple machine-learning classifiers (including the original classifier and additional classifiers trained on different data subsets) into an ensemble system. The ensemble classifier integrates predictions from multiple individual classifiers to produce a final classification, thereby improving precision and recall by leveraging diverse classification perspectives and reducing individual classifier errors.
Solution Approach 2:
The patent segments the training data into different subsets and trains separate classifiers on each subset. This segmentation allows each classifier to specialize in particular data characteristics or error patterns, and the ensemble combines these specialized classifiers to achieve better overall performance than a single general-purpose classifier.
2Measurement precision
If multiple machine-learning classifiers are used to reclassify misclassified elements and classify unclassified elements, then precision and recall improve, but the system complexity and computational resources increase
Solution Approach 1:
The patent employs additional classifiers that are pre-trained specifically on misclassified and unclassified data from the original classifier. These classifiers are prepared in advance to handle specific error cases, allowing the ensemble system to efficiently address classification errors without requiring complex real-time decision logic during inference.
Solution Approach 2:
The ensemble classifier acts as an intermediary that mediates between multiple individual classifiers and the final classification output. It integrates predictions from the original classifier and additional specialized classifiers, resolving conflicts and combining results to produce the final classification decision, thereby managing system complexity while maintaining improved accuracy.
3Reliability
If additional classifiers are trained on misclassified and unclassified data, then identification performance improves, but training time and computational resources increase
Solution Approach 1:
The patent applies local quality by training additional classifiers on specific subsets of data (misclassified data and unclassified data) rather than training all classifiers on the entire dataset. This localized training approach allows each additional classifier to focus on specific error patterns or data characteristics, improving identification performance for particular cases while reducing overall training time compared to retraining all classifiers on all data.
Data Source
AI summary
A machine-learning classification system includes a first machine-learning classifier that classifies each element of a plurality of data items to generate a plurality of classified data items. A second machine-learning classifier identifies misclassified elements of the plurality of classified data items and reclassifies each of the identified misclassified elements to generate a plurality of reclassified data items. A second machine-learning classifier identifies unclassified elements of the plurality of classified data items and classifies each of the identified unclassified elements to generate a plurality of reclassified data items. An ensemble classifier adjusts the classifications of the elements of the plurality of classified data items in response to the plurality of reclassified data items and the plurality of newly-classified elements.