Class-Based Weight Coefficients for Ensemble Accuracy
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Solution Overview
Problem
Existing machine learning ensemble methods face challenges in effectively weighting the outputs of base classifiers, particularly when there is high variability in performance across different classes, which can lead to inconsistent accuracy in classification tasks.
Innovation Solution
A model-agnostic technique is introduced that assigns class-based weight coefficients to each output class in each learner within the ensemble. These coefficients are determined based on the model's performance on each class, generating a dense set of weights that can be applied to various types of base classifiers, including Extreme Learning Machines (ELMs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ensemble methods are used to combine base classifiers, then the system can perform classification tasks, but the accuracy and consistency deteriorate when there is high variability in performance across different classes
Solution Approach 1:
The patent applies local quality by assigning different weight coefficients to each base classifier for each output class individually. Instead of using a uniform weighting approach across all classes, the system creates a tailored weight matrix where each element w_ij represents the weight of classifier i for class j. This allows the ensemble to adapt to the varying performance characteristics of different classifiers on different classes, thereby improving accuracy consistency while handling high variability in class performance.
2Reliability
If uniform weighting is applied to all base classifiers in the ensemble, then the system structure remains simple, but the classification accuracy deteriorates when classifiers have highly variable performance across classes
Solution Approach 1:
The patent implements parameter changes by transitioning from fixed uniform weights to dynamic class-specific weight coefficients. The weight matrix W is constructed based on validation performance metrics, where each weight w_ij is determined by the classifier i's performance on class j. This parameter adaptation allows the system to achieve higher accuracy by adjusting weights according to actual performance characteristics, while the systematic approach to weight calculation keeps the complexity manageable.
3Reliability
If class-based weight coefficients are assigned to each output class in each learner, then the accuracy and consistency of classification tasks are enhanced, but the computational complexity and system complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-computing the weight matrix W during a validation phase before deploying the ensemble for actual classification. The weights are determined by evaluating each classifier's performance on a validation set and calculating the appropriate weight coefficients in advance. This preliminary computation separates the complex weight determination process from the real-time classification process, thereby reducing the computational burden during deployment while maintaining the accuracy benefits of class-based weighting.
Data Source
AI summary
A model-agnostic method for weighting the outputs of base classifiers in machine learning (ML) ensembles. Class-based weight coefficients are assigned to every output class in each learner in the ensemble. A dense set of coefficients is generated for the models in the ensemble by considering the model performance on each class. The approach can be applied to an ensemble of extreme learning machines (ELMs), which are well suited for this approach due to their stochastic, highly varying performance across classes.