Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models

Kotanchek, Mark; Smits, Guido; Vladislavleva, Ekaterina

doi:10.1007/978-0-387-76308-8_12

Cited by 34 publications

(20 citation statements)

References 2 publications

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“…Use of interval arithmetic is proposed in [10], [11] to evolve reliable models that do not have undefined behaviour in their output range. The method calculates output bound recursively for every node of the model, given the bounds of the input arguments.…”

Section: A Interval Arithmeticmentioning

confidence: 99%

“…Averaging the output of diverse models to improve prediction accuracy and using their consensus to assess the trust is proposed in [11]. Different strategies to generate robust and diverse models [11] are: (i) using different function sets (ii) executing independent runs (iii) using different subsets for each generation within a single evolution.…”

Section: Ensemble Of Heterogeneous Modelsmentioning

confidence: 99%

“…Different strategies to generate robust and diverse models [11] are: (i) using different function sets (ii) executing independent runs (iii) using different subsets for each generation within a single evolution. Advantage of these strategies is that the whole available dataset is used for model development compared to traditional approach of dividing the dataset into training, test and validation subsets to mitigate risk of over-fitting.…”

Section: Ensemble Of Heterogeneous Modelsmentioning

confidence: 99%

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Empirical modeling using genetic programming: a survey of issues and approaches

Dabhi

Chaudhary

2014

Nat Comput

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Abstract--In the field of empirical modeling using Genetic Programming (GP), it is important to evolve solution with good generalization ability. Generalization ability of GP solutions get affected by two important issues: bloat and over-fitting. We surveyed and classified existing literature related to different techniques used by GP research community to deal with these issues. We also point out limitation of these techniques, if any. Moreover, the classification of different bloat control approaches and measures for bloat and over-fitting are also discussed. We believe that this work will be useful to GP practitioners in following ways: (i) to better understand concepts of generalization in GP (ii) comparing existing bloat and over-fitting control techniques and (iii) selecting appropriate approach to improve generalization ability of GP evolved solutions.

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Section: A Interval Arithmeticmentioning

confidence: 99%

Section: Ensemble Of Heterogeneous Modelsmentioning

confidence: 99%

Section: Ensemble Of Heterogeneous Modelsmentioning

confidence: 99%

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Empirical modeling using genetic programming: a survey of issues and approaches

Dabhi

Chaudhary

2014

Nat Comput

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“…. , p (s) k to be used as input and the second part showing the values of the output variables per simulation run as displayed in Table 1. input parameters output parameters run Step 4 Supply the Design of Experiments to the commercial Symbolic Regression software package 'DataModeler' [4]. This tool constructs mathematical models using a genetic algorithm [5].…”

Section: Details Of the Individual Stepsmentioning

confidence: 99%

“…Secondly, a software tool is needed that can generate mathematical regression (or symbolic) models given a set of input data. These tools mostly use a so-called genetic algorithm [3,4,5,11].…”

Section: Procedures To Solve the Problemmentioning

confidence: 99%

Behavioral modeling of the dominant dynamics in input-output transfer of linear(ized) circuits

Beelen

Maten

Sihaloho

et al. 2010

Procedia Computer Science

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We present a powerful procedure for determining both the dominant dynamics of the input-output transfer and the corresponding most influential circuit parameters of a linear(ized) circuit. The procedure consists of several steps in which a specific (sub)problem is solved and its solution is used in the next step. We combine regression tools techniques and trade off analysis with recent techniques from Model Order Reduction.

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Revealing Complex Ecological Dynamics via Symbolic Regression

2019

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Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a "dictionary" of possible ecological functional responses opens the door to correctly reverse-engineering ecosystem dynamics, even in the case of poorly informative data, is shown. This strategy is validated using both synthetic and experimental data, and it is found that this strategy is promising for the systematic modeling of complex ecological systems.

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Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models

Cited by 34 publications

References 2 publications

Empirical modeling using genetic programming: a survey of issues and approaches

Empirical modeling using genetic programming: a survey of issues and approaches

Behavioral modeling of the dominant dynamics in input-output transfer of linear(ized) circuits

Revealing Complex Ecological Dynamics via Symbolic Regression

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