Evolutionary machine learning: A survey
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …
problems in a stochastic manner. They can offer a reliable and effective approach to address …
Automatic design of machine learning via evolutionary computation: A survey
Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …
knowledge from data, has been widely applied to practical applications, such as …
Evolutionary deep learning: A survey
As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …
(DL) has achieved great success in many real-world applications and attracted increasing …
Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study
Detecting anomalies in time series data is becoming mainstream in a wide variety of
industrial applications in which sensors monitor expensive machinery. The complexity of this …
industrial applications in which sensors monitor expensive machinery. The complexity of this …
The effects of random undersampling with simulated class imbalance for big data
T Hasanin, T Khoshgoftaar - 2018 IEEE international …, 2018 - ieeexplore.ieee.org
With the recent explosion of big data, real-world data are increasingly being affected by
larger degrees of class imbalance, likely hindering Machine Learning algorithm …
larger degrees of class imbalance, likely hindering Machine Learning algorithm …
A survey on unbalanced classification: How can evolutionary computation help?
Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …
widespread attention from both the academic and industrial communities due mainly to its …
EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification
Learning from imbalanced datasets is highly demanded in real-world applications and a
challenge for standard classifiers that tend to be biased towards the classes with the majority …
challenge for standard classifiers that tend to be biased towards the classes with the majority …
Investigating random undersampling and feature selection on bioinformatics big data
This paper aims to address a key research issue regarding the ECBDL'14 bioinformatics big
data competition. The ECBDL'14 dataset was the big data target in the competition, and it …
data competition. The ECBDL'14 dataset was the big data target in the competition, and it …
Undersampling with support vectors for multi-class imbalanced data classification
Learning from imbalanced data poses significant challenges for the classifier. This becomes
even more difficult, when dealing with multi-class problems. Here relationships among …
even more difficult, when dealing with multi-class problems. Here relationships among …
An approach to data reduction for learning from big datasets: Integrating stacking, rotation, and agent population learning techniques
I Czarnowski, P Jędrzejowicz - Complexity, 2018 - Wiley Online Library
In the paper, several data reduction techniques for machine learning from big datasets are
discussed and evaluated. The discussed approach focuses on combining several …
discussed and evaluated. The discussed approach focuses on combining several …