Composite Machine Learning Model for Unbalanced Data Classification
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Solution Overview
Problem
Conventional machine learning models face challenges when dealing with unbalanced datasets, leading to issues of overfitting or underfitting, and existing approaches to address these problems often result in inefficient processing times, high memory usage, and reduced accuracy.
Innovation Solution
A computer-implemented method generates a composite machine learning model by creating multiple training datasets with balanced data subsets from unbalanced datasets, using machine learning algorithms to train separate models for each category, and combining these models for classification and prediction tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning algorithms are applied to unbalanced datasets, then the model can be trained quickly, but the model suffers from overfitting or underfitting and produces biased outputs
Solution Approach 1:
The patent segments the unbalanced training dataset into multiple balanced subsets by dividing data across multiple categories. Each subset contains balanced representation of different categories, allowing separate model training for each subset. This segmentation resolves the contradiction by enabling accurate multi-category classification without requiring the entire unbalanced dataset to be processed as a whole, thus maintaining processing efficiency while improving model reliability.
Solution Approach 2:
The patent applies local quality by training separate models for each data subset, where each model is optimized for specific category combinations. Instead of training a single model on the entire unbalanced dataset, each local model receives balanced data for its specific subset, improving its accuracy for that local problem while the ensemble of models handles the global unbalanced dataset effectively.
2Reliability
If model complexity is increased to address underfitting on unbalanced datasets, then training accuracy may improve, but processing time and memory usage increase significantly
Solution Approach 1:
The patent segments the training problem into multiple smaller balanced subsets, each trained by a separate model. This segmentation allows each model to be simpler and faster to train, as they only need to handle balanced data for their specific subset rather than the entire complex unbalanced dataset. The time loss is reduced because multiple simple models process data in parallel rather than one complex model processing everything sequentially.
Solution Approach 2:
The patent applies partial action by training models on balanced subsets rather than the complete unbalanced dataset. Each model performs training on a partial balanced subset, which is sufficient for achieving high accuracy on that subset. The excess action is avoided because we only train on the necessary balanced portions rather than attempting to train on all data with increased complexity.
3Reliability
If regularization techniques are applied to prevent overfitting, then model generalization improves, but computational burden on equipment increases
Solution Approach 1:
The patent segments the training into multiple balanced subsets, each trained by separate models. This segmentation reduces the computational burden because each model trains on smaller balanced datasets rather than the entire unbalanced dataset requiring heavy regularization. The energy consumption is reduced while maintaining generalization capability through the ensemble of specialized models, each handling a specific subset with appropriate regularization for its size.
Data Source
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AI summary
Disclosed is a computer-implemented method for classification and/or prediction of data samples on unbalanced datasets, the method comprising: receiving unbalanced training dataset; generating at least one first model and at least one second model using unbalanced training dataset, the generation of the at least one first model comprises generating first training dataset comprising first data subset and second data subset of equal amounts of data; the generation of the at least one second model comprises generating second training dataset comprising third data subset and fourth data subset of equal amounts of data, machine learning algorithm(s) are employed for learning from the first and second training datasets; generating composite model using the first model(s) and the second model(s); employing composite model for classification and/or prediction on unbalanced test dataset for generating output, the output includes at least one classified data and/or a prediction for a data sample.