Unified ML Model Chains for Low-Retraining Prediction
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Solution Overview
Problem
Existing machine learning systems require extensive retraining for different purposes, leading to increased computational costs and potential degradation of model performance, and decisions are often based on ad-hoc reasoning rather than statistical analysis.
Innovation Solution
A data-driven inference system that automatically computes labels for populations using historical data and modularly connects multiple trained machine learning models, enabling self-supervised training and reusing models for various tasks, improving accuracy and efficiency by propagating improvements through model chains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are retrained for different purposes, then model accuracy for specific tasks is improved, but computational costs increase and training time is extended
Solution Approach 1:
The patent implements a unified machine learning model that performs multiple classification tasks simultaneously through multi-label classification. Instead of training separate models for different purposes (e.g., fraud detection, credit risk assessment, customer segmentation), a single model is trained to handle all these tasks concurrently, eliminating the need for repeated retraining while maintaining accuracy across all functions.
Solution Approach 2:
The patent combines multiple classification objectives into a single unified model by integrating multiple label prediction tasks. The model processes input data once and generates multiple classification outputs simultaneously, merging what would traditionally require separate model training processes into a single efficient operation that reduces computational overhead.
2Measurement precision
If machine learning models are retrained for different purposes, then task-specific performance is improved, but model deployment time increases
Solution Approach 1:
The unified model is designed to perform multiple classification tasks simultaneously, allowing the system to deploy a single model that handles fraud detection, credit risk assessment, and other financial services tasks. This eliminates the time required to sequentially deploy multiple separate models for different purposes.
3Measurement precision
If multiple separate machine learning models are maintained for different tasks, then task specialization is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple specialized classification tasks into a single unified model architecture. Instead of maintaining separate models for fraud detection, credit risk assessment, and customer segmentation, the system uses one integrated model that handles all these tasks through multi-label classification, significantly reducing system complexity while preserving task-specific performance.
Solution Approach 2:
The unified model serves multiple specialized functions simultaneously, providing task specialization without requiring separate model deployments. The model can predict multiple labels for each input record, enabling it to perform fraud detection, credit risk assessment, and other financial services tasks within a single unified framework.
4Adaptability or versatility
If extensive retraining is performed for different machine learning purposes, then model adaptability is improved, but productivity decreases
Solution Approach 1:
The unified model achieves high adaptability by being trained on diverse data covering multiple classification tasks simultaneously. During training, the model learns patterns relevant to fraud detection, credit risk assessment, and other financial services tasks concurrently, enabling it to adapt to different purposes without requiring separate retraining processes, thereby maintaining high training efficiency.
Data Source
AI summary
A method for generating a chain of machine learning models includes: receiving a data sample including one or more features and a target property; identifying, by a processor of a computer system, an unsupervised machine learning model trained to classify data samples based on the one or more features, independently of the target property, into a plurality of clusters; classifying the data sample based on the one or more features using the unsupervised machine learning model to compute a cluster; identifying, by the processor, a supervised machine learning model corresponding to the cluster; and computing a value for the target property by supplying the data sample to the supervised machine learning model.