Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

As machine learning is widely applied to improve the efficiency and effectiveness of
manufacturing systems, the automated selection of appropriate algorithms and …

Over the last decade, the long-running endeavour to automate high-level processes in
machine learning (ML) has risen to mainstream prominence, stimulated by advances in …

Automated machine learning (AutoML) allows for selecting, parametrizing, and composing
learning algorithms for a given data set. While resources play a pivotal role in neural …

Automated machine learning (AutoML) aims for constructing machine learning (ML)
pipelines automatically. Many studies have investigated efficient methods for algorithm …

Automated machine learning pipeline (ML) composition and optimisation aim at automating
the process of finding the most promising ML pipelines within allocated resources (ie, time …

In this paper, we propose an evolutionary learning approach for flexible identification of
custom composite models for classification problems. To solve this problem in an efficient …

Machine learning (ML) pipeline composition and optimisation have been studied to seek
multi-stage ML models' ie preprocessor-inclusive, that are both valid and well-performing …

Automation of machine learning model development is increasingly becoming an
established research area. While automated model selection and automated data pre …