144 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Johannes Kepler University of LinzAT14 papers
- Universidade Federal do ABCBR7 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- University of PortsmouthGB4 papers
- Institut d'Astrophysique de ParisFR3 papers
- Research Center for Non Destructive Testing (Austria)AT3 papers
- Sorbonne UniversitéFR3 papers
- University of OxfordGB3 papers
- Boston Children's HospitalUS2 papers
- Harvard UniversityUS2 papers
- Software Competence Center Hagenberg (Austria)AT2 papers
- Technische Hochschule UlmDE2 papers
22 papers · 1 filter
A Comparative Study of Model Selection Criteria for Symbolic Regression
Ali Soltani, Gabriel Kronberger, Fabricio Olivetti de Franca +2
Effective model selection is critical in symbolic regression (SR) to identify mathematical expressions that balance accuracy and complexity, and have low expected error on unseen d…
Introduction to Symbolic Regression in the Physical Sciences
Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira +1
Symbolic regression (SR) has emerged as a powerful method for uncovering interpretable mathematical relationships from data, offering a novel route to both scientific discovery and…
Can Synthetic Data Improve Symbolic Regression Extrapolation Performance?
Fitria Wulandari Ramlan, Colm O'Riordan, Gabriel Kronberger +1
Many machine learning models perform well when making predictions within the training data range, but often struggle when required to extrapolate beyond it. Symbolic regression (SR…
Call for Action: towards the next generation of symbolic regression benchmark
Guilherme S. Imai Aldeia, Hengzhe Zhang, Geoffrey Bomarito +5
Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity o…
rEGGression: an Interactive and Agnostic Tool for the Exploration of Symbolic Regression Models
Fabricio Olivetti de Franca, Gabriel Kronberger
Regression analysis is used for prediction and to understand the effect of independent variables on dependent variables. Symbolic regression (SR) automates the search for non-linea…
Improving Genetic Programming for Symbolic Regression with Equality Graphs
Fabricio Olivetti de Franca, Gabriel Kronberger
The search for symbolic regression models with genetic programming (GP) has a tendency of revisiting expressions in their original or equivalent forms. Repeatedly evaluating equiva…
Identifying Differential Equations to predict Blood Glucose using Sparse Identification of Nonlinear Systems
David Jödicke, Daniel Parra, Gabriel Kronberger +1
Describing dynamic medical systems using machine learning is a challenging topic with a wide range of applications. In this work, the possibility of modeling the blood glucose leve…
Shape-constrained Symbolic Regression with NSGA-III
Christian Haider
Shape-constrained symbolic regression (SCSR) allows to include prior knowledge into data-based modeling. This inclusion allows to ensure that certain expected behavior is better re…
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization
Lukas Kammerer, Gabriel Kronberger, Michael Kommenda
Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems. We improve the accuracy of FFX by adding parameters to the arguments of nonlin…
Comparing Shape-Constrained Regression Algorithms for Data Validation
Florian Bachinger, Gabriel Kronberger
Industrial and scientific applications handle large volumes of data that render manual validation by humans infeasible. Therefore, we require automated data validation approaches t…
Cluster Analysis of a Symbolic Regression Search Space
Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu +3
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space. The motivation for this work is to imp…
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu +3
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. Such scenarios often require spe…
Understanding and Preparing Data of Industrial Processes for Machine Learning Applications
Philipp Fleck, Manfred Kügel, Michael Kommenda
Industrial applications of machine learning face unique challenges due to the nature of raw industry data. Preprocessing and preparing raw industrial data for machine learning appl…
Preprocessing and Modeling of Radial Fan Data for Health State Prediction
Florian Holzinger, Michael Kommenda
Monitoring critical components of systems is a crucial step towards failure safety. Affordable sensors are available and the industry is in the process of introducing and extending…
Optimization Networks for Integrated Machine Learning
Michael Kommenda, Johannes Karder, Andreas Beham +4
Optimization networks are a new methodology for holistically solving interrelated problems that have been developed with combinatorial optimization problems in mind. In this contri…
Complexity Measures for Multi-objective Symbolic Regression
Michael Kommenda, Andreas Beham, Michael Affenzeller +1
Multi-objective symbolic regression has the advantage that while the accuracy of the learned models is maximized, the complexity is automatically adapted and need not be specified…
On the Effectiveness of Genetic Operations in Symbolic Regression
Bogdan Burlacu, Michael Affenzeller, Michael Kommenda
This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the…
Data Aggregation for Reducing Training Data in Symbolic Regression
Lukas Kammerer, Gabriel Kronberger, Michael Kommenda
The growing volume of data makes the use of computationally intense machine learning techniques such as symbolic regression with genetic programming more and more impractical. This…
Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-valued Optimization Problem
Erik Pitzer, Gabriel Kronberger
The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization…
Concept Drift Detection with Variable Interaction Networks
Jan Zenisek, Gabriel Kronberger, Josef Wolfartsberger +2
The current development of today's production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance. By this means, the co…
Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications
Florian Bachinger, Gabriel Kronberger
With the increasing number of created and deployed prediction models and the complexity of machine learning workflows we require so called model management systems to support data…
Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables
Gabriel Kronberger, Michael Kommenda, Andreas Promberger +1
Friction systems are mechanical systems wherein friction is used for force transmission (e.g. mechanical braking systems or automatic gearboxes). For finding optimal and safe desig…