A review of feature selection methods based on meta-heuristic algorithms
Feature selection is a real-world problem that finds a minimal feature subset from an original
feature set. A good feature selection method, in addition to selecting the most relevant …
feature set. A good feature selection method, in addition to selecting the most relevant …
A feature selection algorithm of decision tree based on feature weight
HF Zhou, JW Zhang, YQ Zhou, XJ Guo… - Expert Systems with …, 2021 - Elsevier
In order to improve the classification accuracy, a preprocessing step is used to pre-filter
some redundant or irrelevant features before decision tree construction. And a new feature …
some redundant or irrelevant features before decision tree construction. And a new feature …
A hybrid feature selection method based on information theory and binary butterfly optimization algorithm
Feature selection is the problem of finding the optimal subset of features for predicting class
labels by removing irrelevant or redundant features. S-shaped Binary Butterfly Optimization …
labels by removing irrelevant or redundant features. S-shaped Binary Butterfly Optimization …
Feature-specific mutual information variation for multi-label feature selection
Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …
regarding multi-label data, which attracts wide attention for feature selection. Feature …
A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification
The ubiquitous usage of feature selection in search space optimization, information retrieval,
data mining, signal processing, software fault prediction, and bioinformatics is paramount to …
data mining, signal processing, software fault prediction, and bioinformatics is paramount to …
Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection
K Chen, FY Zhou, XF Yuan - Expert Systems with Applications, 2019 - Elsevier
The “curse of dimensionality” is one of the largest problems that influences the quality of the
optimization process in most data mining, pattern recognition, and machine learning tasks …
optimization process in most data mining, pattern recognition, and machine learning tasks …
Feature selection in image analysis: a survey
Image analysis is a prolific field of research which has been broadly studied in the last
decades, successfully applied to a great number of disciplines. Since the apparition of Big …
decades, successfully applied to a great number of disciplines. Since the apparition of Big …
Distinguishing two types of labels for multi-label feature selection
Multi-label feature selection plays an important role in pattern recognition, which can
improve multi-label classification performance. In traditional multi-label feature selection …
improve multi-label classification performance. In traditional multi-label feature selection …
Feature selection with maximal relevance and minimal supervised redundancy
Y Wang, X Li, R Ruiz - IEEE Transactions on Cybernetics, 2022 - ieeexplore.ieee.org
Feature selection (FS) for classification is crucial for large-scale images and bio-microarray
data using machine learning. It is challenging to select informative features from high …
data using machine learning. It is challenging to select informative features from high …
R2CI: Information theoretic-guided feature selection with multiple correlations
Abstract Information theoretic-guided feature selection approaches (ITFSs), which exploit the
uncertainty of information to measure the correlation of features, aim to select the most …
uncertainty of information to measure the correlation of features, aim to select the most …