Supervised Classifier Training With Risk-Guided Iteration
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Solution Overview
Problem
Existing supervised binary machine learning classifiers suffer from poor class separation leading to low precision and recall, and lack a measure to estimate the deployment risk of false negatives, which is particularly problematic in industrial applications where misclassifications can lead to increased waste and costs.
Innovation Solution
A method for training a classifier model that includes obtaining a seed set of labeled data, training the model, evaluating its quality using a test dataset, determining a global risk value and reward value, and iteratively updating parameters until the risk or reward meets predetermined limits, allowing for a priori estimation of deployment risk and minimizing training effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing supervised binary machine learning classifiers are used for data classification, then classification can be performed, but poor separation of classes leads to low precision and recall
Solution Approach 1:
The patent implements an iterative feedback mechanism where the classifier is repeatedly trained on augmented datasets. After each training iteration, the classifier performance is evaluated on a test set, and the results feed back into the data augmentation process. This feedback loop continuously improves the classifier's separation of classes, progressively enhancing both precision and recall until convergence is achieved.
Solution Approach 2:
The patent dynamically adjusts training parameters including learning rate, number of training iterations, and data augmentation intensity based on performance metrics. By monitoring precision and recall across iterations, the system automatically tunes parameters to optimize class separation, transforming the static training process into an adaptive parameter optimization process that resolves the contradiction between precision and class separation quality.
2Productivity
If existing classifiers are used without risk estimation, then classification can be performed, but no measure to estimate deployment risk of false negatives is available
Solution Approach 1:
The patent performs preliminary risk assessment before classifier deployment by evaluating performance on a held-out test set that represents production conditions. The system calculates metrics such as false negative rate, precision, and recall in advance, establishing a risk profile before the classifier is deployed to production. This preliminary action enables informed deployment decisions and allows for continuous monitoring during actual use.
3Measurement precision
If large data pools are used for training to enhance classification quality, then classification accuracy improves, but this is not always possible for industrial applications with limited data availability
Solution Approach 1:
The patent creates synthetic copies of limited training data through multiple data augmentation techniques. These include generating augmented versions of existing samples by applying transformations, creating pseudo-labels from uncertain predictions, and synthesizing new training examples from data distributions. This copying process exponentially increases the effective training data quantity while preserving the original data's informational content, enabling high-accuracy classification even with initially limited data.
Solution Approach 2:
The patent applies partial data augmentation strategies that selectively enhance specific portions of the training data based on their informational value and class representation. Rather than uniformly processing all data, the system identifies and augments the most critical samples - those that provide the highest learning benefit - thereby achieving significant accuracy improvement with minimal additional processing effort, effectively applying partial action to maximize impact.
4Measurement precision
If manual labeling of large datasets is performed to improve training data quality, then classification performance improves, but manual effort and time increase significantly
Solution Approach 1:
The patent implements self-service labeling where the system automatically generates training data and labels through iterative processes. The classifier makes predictions on unlabeled data, and these predictions are used to create training examples without human intervention. The system self-augments the training dataset by generating synthetic labeled examples from the data distribution, eliminating the need for manual annotators while maintaining high training data quality through automated information extraction and synthesis.
Data Source
AI summary
The present invention relates to information retrieval. In order to facilitate a search and identification of documents, there is provided a computer-implemented method for training a classifier model for data classification in response to a search query. The computer-implemented method comprises:a) obtaining a dataset that comprises a seed set of labeled data representing a training dataset;b) training the classifier model by using the training dataset to fit parameters of the classifier model;c) evaluating a quality of the classifier model using a test dataset that comprises unlabeled data from the obtained dataset to generate a classifier confidence score indicative of a probability of correctness of the classifier model working on the test dataset;d) determining a global risk value of misclassification and a reward value based on the classifier confidence score on the test dataset;e) iteratively updating the parameters of the classifier model and performing steps b) to d) until the global risk value falls within a predetermined risk limit value or an expected reward value is reached.