Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …
must be carefully chosen and which often considerably impact performance. To avoid a time …
Open-source machine learning in computational chemistry
A Hagg, KN Kirschner - Journal of Chemical Information and …, 2023 - ACS Publications
The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
Eight years of AutoML: categorisation, review and trends
Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …
applied in a large number of application domains. However, apart from the required …
Intelligent ensembling of auto-ML system outputs for solving classification problems
Abstract Automatic Machine Learning (Auto-ML) tools enable the automatic solution of real-
world problems through machine learning techniques. These tools tend to be more time …
world problems through machine learning techniques. These tools tend to be more time …
AutoOC: Automated multi-objective design of deep autoencoders and one-class classifiers using grammatical evolution
L Ferreira, P Cortez - Applied Soft Computing, 2023 - Elsevier
Abstract One-Class Classification (OCC) corresponds to a subclass of unsupervised
Machine Learning (ML) that is valuable when labeled data is non-existent. In this paper, we …
Machine Learning (ML) that is valuable when labeled data is non-existent. In this paper, we …
NER sequence embedding of unified medical corpora to incorporate semantic intelligence in big data healthcare diagnostics
Clinical diagnosis is a challenging task for which high expertise is required at the doctors'
end. It is recognized that technology integration with the clinical domain would facilitate the …
end. It is recognized that technology integration with the clinical domain would facilitate the …
KD SENSO-MERGER: An architecture for semantic integration of heterogeneous data
This paper presents KD SENSO-MERGER, a novel Knowledge Discovery (KD) architecture
that is capable of semantically integrating heterogeneous data from various sources of …
that is capable of semantically integrating heterogeneous data from various sources of …
[HTML][HTML] Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity
The process of extracting valuable and novel insights from raw data involves a series of
complex steps. In the realm of Automated Machine Learning (AutoML), a significant research …
complex steps. In the realm of Automated Machine Learning (AutoML), a significant research …
Automatic annotation of protected attributes to support fairness optimization
Recent research has shown that the unaware automation of high-risk decision-making tasks
can result in unfair decisions being made. The most common approaches to address this …
can result in unfair decisions being made. The most common approaches to address this …
Evolving machine learning workflows through interactive AutoML
Automatic workflow composition (AWC) is a relevant problem in automated machine
learning (AutoML) that allows finding suitable sequences of preprocessing and prediction …
learning (AutoML) that allows finding suitable sequences of preprocessing and prediction …