6 Result(s)
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Marcel Wever
Machine Learning
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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 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
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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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...
