Automated Symbolic Regression for Data Model Discovery
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Solution Overview
Problem
Conventional symbolic regression techniques require human intervention to specify mathematical models, which can be a bottleneck in analyzing complex data sets, and struggle to automatically identify relationships between data sets.
Innovation Solution
Implementing automated symbolic regression systems that reduce data sets into representative models composed of fundamental equations, allowing for the analysis and comparison of data models to determine relationships and similarities without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional symbolic regression techniques are used, then mathematical models can be discovered from data sets, but human intervention is required to specify the models which creates a bottleneck in analysis
Solution Approach 1:
The system performs self-service by automatically generating mathematical models from raw data without requiring human specification. The symbolic regression engine autonomously discovers relationships, generates candidate models, validates them against data, and iterates to find optimal models, eliminating the need for human operators to manually specify model forms.
Solution Approach 2:
The system performs preliminary action by pre-defining a comprehensive library of mathematical functions and operators that can be automatically applied to data. This preliminary preparation of computational tools enables the automated model discovery process to proceed without human intervention, as the system has already assembled the necessary mathematical building blocks.
2Adaptability or versatility
If conventional symbolic regression systems are used, then model parameters can be derived, but the systems struggle to automatically identify relationships between multiple data sets
Solution Approach 1:
The system achieves universality by designing a multi-functional symbolic regression engine that can perform multiple tasks: discovering relationships within individual data sets, comparing relationships across multiple data sets, identifying patterns, and generating models. This single system handles diverse analytical functions that would otherwise require separate tools, enabling automatic relationship identification across data sets.
Solution Approach 2:
The system applies segmentation by breaking down the complex task of relationship identification into discrete components: extracting mathematical models from each data set, representing models in standardized form, comparing model structures, and identifying relationships between models. This segmentation makes the overall complex analysis manageable and automatable.
3Productivity
If automated symbolic regression is implemented, then analysis efficiency is improved, but the system must process and compare large numbers of mathematical expressions
Solution Approach 1:
The system extracts only the essential mathematical structures from data sets, representing models in standardized symbolic form that captures the core relationships. By extracting and representing only the critical model structures rather than processing all possible variations, the system improves analysis efficiency while managing computational complexity through focused comparison of essential model features.
Data Source
AI summary
Systems and methods of searching for related data sets are provided. Multivariate data sets can be input as queries into a data set search engine. According to one embodiment, the input data set is automatically reduced to a set of best fit data models of minimum complexity that represent the data set. The data model is then compared to other data models to not only identify similarity between the models, but also to identify the particulars of why the data models are related. Similar data model results can be analyzed to determine the quality of each returned data model based on an information scores. These results can be displayed graphically as a topographical map of nodes and edge. Each node can represent a data model and each edge can reflect the similarity between the nodes.