47 citations · 150 across the 18 of their papers we have counts for
10 papers · 1 filter
A Probabilistic Transformation of Distance-Based Outliers
David Muhr, Michael Affenzeller, Josef Küng
The scores of distance-based outlier detection methods are difficult to interpret, making it challenging to determine a cut-off threshold between normal and outlier data points wit…
OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language
David Muhr, Michael Affenzeller, Anthony D. Blaom
OutlierDetection.jl is an open-source ecosystem for outlier detection in Julia. It provides a range of high-performance outlier detection algorithms implemented directly in Julia.…
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
Hao Wang, Kaifeng Yang, Michael Affenzeller
Hypervolume improvement (HVI) is commonly employed in multi-objective Bayesian optimization algorithms to define acquisition functions due to its Pareto-compliant property. Rather…
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…
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…
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…
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression
Bogdan Burlacu, Lukas Kammerer, Michael Affenzeller +1
We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symb…