When do neural nets outperform boosted trees on tabular data?
Tabular data is one of the most commonly used types of data in machine learning. Despite
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …
A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research
The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …
issues in machine learning. While seminal work focused on establishing class overlap as a …
Meta-features for meta-learning
Meta-learning is increasingly used to support the recommendation of machine learning
algorithms and their configurations. These recommendations are made based on meta-data …
algorithms and their configurations. These recommendations are made based on meta-data …
A meta-learning approach of optimisation for spatial prediction of landslides
Optimisation plays a key role in the application of machine learning in the spatial prediction
of landslides. The common practice in optimising landslide prediction models is to search for …
of landslides. The common practice in optimising landslide prediction models is to search for …
Against the “one method fits all data sets” philosophy for comparison studies in methodological research
C Strobl, F Leisch - Biometrical Journal, 2024 - Wiley Online Library
Many methodological comparison studies aim at identifying a single or a few “best
performing” methods over a certain range of data sets. In this paper we take a different …
performing” methods over a certain range of data sets. In this paper we take a different …
Optimization on selecting XGBoost hyperparameters using meta‐learning
T Lima Marinho, DC do Nascimento… - Expert …, 2024 - Wiley Online Library
With computational evolution, there has been a growth in the number of machine learning
algorithms and they became more complex and robust. A greater challenge is upon faster …
algorithms and they became more complex and robust. A greater challenge is upon faster …
Automated model selection for multivariate anomaly detection in manufacturing systems
H Engbers, M Freitag - Journal of Intelligent Manufacturing, 2024 - Springer
As machine learning is widely applied to improve the efficiency and effectiveness of
manufacturing systems, the automated selection of appropriate algorithms and …
manufacturing systems, the automated selection of appropriate algorithms and …
Autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithms
The recent evolution of machine learning (ML) algorithms and the high level of expertise
required to use them have fuelled the demand for non-experts solutions. The selection of an …
required to use them have fuelled the demand for non-experts solutions. The selection of an …
PnT: Born-again tree-based model via fused decision path encoding
Decision forests, such as random forest (RF) are widely used for tabular data, mainly due to
their predictive performance and ease of usage. However, given that the forest's trees may …
their predictive performance and ease of usage. However, given that the forest's trees may …
A study on ensemble learning for time series forecasting and the need for meta-learning
J Gastinger, S Nicolas, D Stepić… - … Joint Conference on …, 2021 - ieeexplore.ieee.org
The contribution of this work is twofold:(1) We introduce a collection of ensemble methods
for time series forecasting to combine predictions from base models. We demonstrate …
for time series forecasting to combine predictions from base models. We demonstrate …