Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019)
Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …
dimension of the feature set while maintaining the accuracy of the performance is the main …
A review of the modification strategies of the nature inspired algorithms for feature selection problem
This survey is an effort to provide a research repository and a useful reference for
researchers to guide them when planning to develop new Nature-inspired Algorithms …
researchers to guide them when planning to develop new Nature-inspired Algorithms …
Simulated annealing-based dynamic step shuffled frog leaping algorithm: Optimal performance design and feature selection
The shuffled frog leaping algorithm is a new optimization algorithm proposed to solve the
combinatorial optimization problem, which effectively combines the memetic algorithm …
combinatorial optimization problem, which effectively combines the memetic algorithm …
[HTML][HTML] Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods
The evaluation of feature selection methods for text classification with small sample datasets
must consider classification performance, stability, and efficiency. It is, thus, a multiple …
must consider classification performance, stability, and efficiency. It is, thus, a multiple …
Feature selection based on artificial bee colony and gradient boosting decision tree
H Rao, X Shi, AK Rodrigue, J Feng, Y Xia… - Applied Soft …, 2019 - Elsevier
Data from many real-world applications can be high dimensional and features of such data
are usually highly redundant. Identifying informative features has become an important step …
are usually highly redundant. Identifying informative features has become an important step …
Binary grasshopper optimisation algorithm approaches for feature selection problems
Feature Selection (FS) is a challenging machine learning-related task that aims at reducing
the number of features by removing irrelevant, redundant and noisy data while maintaining …
the number of features by removing irrelevant, redundant and noisy data while maintaining …
Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection
Plant disease, especially crop plants, is a major threat to global food security since many
diseases directly affect the quality of the fruits, grains, and so on, leading to a decrease in …
diseases directly affect the quality of the fruits, grains, and so on, leading to a decrease in …
A survey on evolutionary computation approaches to feature selection
Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …
dimensionality of the data and increase the performance of an algorithm, such as a …
Selective opposition based grey wolf optimization
The use of metaheuristics is widespread for optimization in both scientific and industrial
problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf …
problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf …
Partial reinforcement optimizer: an evolutionary optimization algorithm
In this paper, a novel evolutionary optimization algorithm, named Partial Reinforcement
Optimizer (PRO), is introduced. The major idea behind the PRO comes from a psychological …
Optimizer (PRO), is introduced. The major idea behind the PRO comes from a psychological …