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Article
Open AccessAlgorithm selection on a meta level
The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selec...
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Article
Open AccessNaive automated machine learning
An essential task of automated machine learning ( \(\text {AutoML}\) AutoML )...
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Chapter and Conference Paper
Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets
In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anom...
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Article
Open AccessA comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials
Heated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially de...
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Article
Open AccessA flexible class of dependence-aware multi-label loss functions
The idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, howev...
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Chapter and Conference Paper
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data
Algorithm selection refers to the task of automatically selecting the most suitable algorithm for solving an instance of a computational problem from a set of candidate algorithms. Here, suitability is typical...
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Chapter and Conference Paper
Extreme Algorithm Selection with Dyadic Feature Representation
Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, e.g., choosing solver...
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Chapter and Conference Paper
LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification
In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification, where an instance is assigned a single label. Binary relevance (BR) learning...
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Chapter and Conference Paper
Hybrid Ranking and Regression for Algorithm Selection
Algorithm selection (AS) is defined as the task of automatically selecting the most suitable algorithm from a set of candidate algorithms for a specific instance of an algorithmic problem class. While suitabil...
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Chapter and Conference Paper
AutoML for Predictive Maintenance: One Tool to RUL Them All
Automated machine learning (AutoML) deals with the automatic composition and configuration of machine learning pipelines, including the selection and parametrization of preprocessors and learning algorithms. W...
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Article
ML-Plan: Automated machine learning via hierarchical planning
Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approac...
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Chapter and Conference Paper
Reduction Stumps for Multi-class Classification
Multi-class classification problems are often solved via reduction, i.e., by breaking the original problem into a set of presumably simpler subproblems (and aggregating the solutions of these problems later on). ...
