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 …
Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model
Q Li, Z Song - Journal of Cleaner Production, 2023 - Elsevier
By replacing cement in concrete production with rice husk ash (RHA), the amount of cement
used and its environmental impact can be reduced. The objective of this study is to …
used and its environmental impact can be reduced. The objective of this study is to …
A multiobjective evolutionary nonlinear ensemble learning with evolutionary feature selection for silicon prediction in blast furnace
In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron
is of great significance for maintaining stable furnace conditions, improving hot metal quality …
is of great significance for maintaining stable furnace conditions, improving hot metal quality …
A novel ensemble learning paradigm for medical diagnosis with imbalanced data
N Liu, X Li, E Qi, M Xu, L Li, B Gao - IEEE Access, 2020 - ieeexplore.ieee.org
With the help of machine learning (ML) techniques, the possible errors made by the
pathologists and physicians, such as those caused by inexperience, fatigue, stress and so …
pathologists and physicians, such as those caused by inexperience, fatigue, stress and so …
Loan default prediction using a credit rating-specific and multi-objective ensemble learning scheme
For the consumer lending industry, credit risk assessment is a crucial task for evaluating the
default probability of loan applications given the potential loss caused by default. In …
default probability of loan applications given the potential loss caused by default. In …
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 …
On supervised class-imbalanced learning: An updated perspective and some key challenges
S Das, SS Mullick, I Zelinka - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …
traditional machine learning and the emerging deep learning research communities. A …
Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data
In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for
the classification of hyperspectral images with limited training data. Our proposition is based …
the classification of hyperspectral images with limited training data. Our proposition is based …
Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting
Streamflow series forecasting composes a fundamental step in planning electric energy
production for hydroelectric plants. In Brazil, such plants produce almost 70% of the total …
production for hydroelectric plants. In Brazil, such plants produce almost 70% of the total …