ML Inference Reliability Using Density-Based Input Classification
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Solution Overview
Problem
Evaluating and monitoring machine learning models is resource-intensive, particularly for complex models with large input sizes, and there is a need for a more efficient method to assess the reliability of model predictions.
Innovation Solution
Perform correlation analysis on input data to identify highly correlated features, create density maps to find low-density areas, label records accordingly, and train a classifier to distinguish between low-density and high-density records, providing an indication of prediction reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If evaluation and monitoring of machine learning models rely on comprehensive behavior analysis of outcomes as function of inputs, then measurement precision is improved, but use of energy and computational resources increases significantly
Solution Approach 1:
The patent segments the input feature space into high-density and low-density regions using clustering algorithms. By dividing the comprehensive evaluation task into region-specific evaluations, the system achieves accurate model monitoring while reducing overall computational burden. High-density regions receive detailed analysis while low-density regions use simplified evaluation methods.
Solution Approach 2:
The patent applies different evaluation strategies to different regions of the input space. High-density regions undergo comprehensive outcome analysis while low-density regions use surrogate models or simplified metrics. This local differentiation maintains measurement precision where it matters most while reducing energy consumption in less critical areas.
2Reliability
If complex machine learning models with large input sizes are evaluated comprehensively, then reliability of model predictions is improved, but productivity decreases due to resource intensity
Solution Approach 1:
The patent performs preliminary clustering of the input feature space before comprehensive model evaluation. By pre-identifying high-density regions where predictions matter most, the system can focus computational resources on these critical areas, maintaining prediction reliability while improving evaluation productivity through prioritized processing.
Solution Approach 2:
The patent creates simplified surrogate models or approximate representations for low-density regions. These copies allow rapid evaluation without full model computation, thereby improving productivity while maintaining sufficient reliability for less critical input regions through the use of validated approximation methods.
3Measurement precision
If density analysis is performed on all feature pairs to identify low-density areas, then measurement precision of data distribution understanding is improved, but device complexity increases
Solution Approach 1:
The patent extracts and focuses analysis on the most relevant feature pairs that contribute most to identifying meaningful density patterns. By selecting key feature combinations rather than analyzing all possible pairs, the system achieves accurate data distribution understanding while reducing computational complexity and resource requirements.
Solution Approach 2:
The patent dynamically adjusts density thresholds and analysis parameters based on the specific characteristics of the dataset and model. This adaptive approach allows the system to maintain high measurement precision for data distribution understanding while optimizing computational complexity by adjusting analysis depth and granularity according to actual data needs.
Data Source
AI summary
A method, system, and computer program product for classifying input data of a machine learning model. The method includes obtaining a dataset. The method also includes determining pairwise correlations of the set of features using their values in the dataset. The method also includes selecting one or more pairs of features that are highly correlated. The method also includes creating a density map that contains a set of points. The method also includes determining a low-density area on the density map having a low-density of points from the density analysis. The method also includes identifying records of the dataset that belong to the determined low-density areas. The method also includes labeling the identified records as low-density and labeling the remaining records of the dataset as high-density. The method also includes training a classifier to classify an input record having the set of features as a low-density or high-density record.