7 Result(s)
within
Chapter
Marcel Wever
Computer Science
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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). ...
