Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms
Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with
diverse cellular processes, such as cell–cell communication, and gene expression …
diverse cellular processes, such as cell–cell communication, and gene expression …
MRMD2. 0: a python tool for machine learning with feature ranking and reduction
S He, F Guo, Q Zou - Current Bioinformatics, 2020 - ingentaconnect.com
Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Dimensionality reduction is the key issue of the machine learning process. It does not only …
A multiobjective intelligent decision-making method for multistage placement of PMU in power grid enterprises
B Cao, Y Yan, Y Wang, X Liu, JCW Lin… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The wide area measurement system (WAMS) based on synchronous phasor measurement
technology plays an increasingly important role in dynamic monitoring and wide area …
technology plays an increasingly important role in dynamic monitoring and wide area …
Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks
Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary
algorithms to approximate the optimal solutions of large-scale multiobjective optimization …
algorithms to approximate the optimal solutions of large-scale multiobjective optimization …
A duplication analysis-based evolutionary algorithm for biobjective feature selection
Feature selection is a complex optimization problem with important real-world applications.
Normally, its main target is to reduce the dimensionality of the dataset and increase the …
Normally, its main target is to reduce the dimensionality of the dataset and increase the …
Hyperplane assisted evolutionary algorithm for many-objective optimization problems
In many-objective optimization problems (MaOPs), forming sound tradeoffs between
convergence and diversity for the environmental selection of evolutionary algorithms is a …
convergence and diversity for the environmental selection of evolutionary algorithms is a …
Surrogate sample-assisted particle swarm optimization for feature selection on high-dimensional data
X Song, Y Zhang, D Gong, H Liu… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
With the increase of the number of features and the sample size, existing feature selection
(FS) methods based on evolutionary optimization still face challenges such as the “curse of …
(FS) methods based on evolutionary optimization still face challenges such as the “curse of …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
On the estimation of pareto front and dimensional similarity in many-objective evolutionary algorithm
Evolutionary algorithms have been proven to be effective in solving multi-objective
optimization problems. However, their performance deteriorates progressively in handling …
optimization problems. However, their performance deteriorates progressively in handling …
Ensemble many-objective optimization algorithm based on voting mechanism
Sorting solutions play a key role in using evolutionary algorithms (EAs) to solve many-
objective optimization problems (MaOPs). Generally, different solution-sorting methods …
objective optimization problems (MaOPs). Generally, different solution-sorting methods …