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Analysis and Evaluation of Feature Selection and Feature Extraction Methods
Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest....
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Deep Feature selection
Feature selection plays a crucial role in machine learning by identifying the most relevant and informative features from the input data, leading to...
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Feature selection for label distribution learning under feature weight view
Label Distribution Learning (LDL) is a fine-grained learning paradigm that addresses label ambiguity, yet it confronts the curse of dimensionality....
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Hybrid feature selection method for predicting software defect
To address the challenges associated with the abundance of features in software datasets, this study proposes a novel hybrid feature selection method...
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Feature selection based on probability and mathematical expectation
Many kinds of information entropy are employed for feature selection, but they lack corresponding probabilities to interpret; Despite many...
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Uncertainty Measure-Based Incremental Feature Selection For Hierarchical Classification
In the era of big data, there exist complex structure between different classes labels. Hierarchical structure, among others, has become a...
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Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection
Breast cancer has been one of the leading causes of death among women in the world. Early detection of this disease can save patient’s lives and...
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Feature selection with clustering probabilistic particle swarm optimization
Dealing with high-dimensional data poses a significant challenge in machine learning. To address this issue, researchers have proposed feature...
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Fuzzy information gain ratio-based multi-label feature selection with label correlation
Multi-label feature selection aims to mitigate the curse of dimensionality in multi-label data by selecting a smaller subset of features from the...
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Sparse low-redundancy multi-label feature selection with constrained laplacian rank
As one of the crucial methods for data dimensionality reduction, multi-label feature selection aims to eliminate irrelevant and redundant features...
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Feature selection on quantum computers
In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is...
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Unsupervised Feature Selection Approach for Smartwatches
Traditional feature selection methods can be time-consuming and labor-intensive, especially with large datasets. This study’s unsupervised feature...
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Binary arithmetic optimization algorithm for feature selection
Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some...
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Parallel dual-channel multi-label feature selection
In the process of multi-label learning, feature selection methods are often adopted to solve the high-dimensionality problem in feature spaces. Most...
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Sparse semi-supervised multi-label feature selection based on latent representation
With the rapid development of the Internet, there are a large number of high-dimensional multi-label data to be processed in real life. To save...
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A Binary Chaotic Transient Search Optimization Algorithm for Enhancing Feature Selection
Real-world data mining problems require feature selection to improve efficiency and accuracy. Due to not considering characteristics of the FS...
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Reinforced feature selection using Q-learning based on collaborative agents
Reinforced feature selection (RFS) applies reinforcement learning to feature selection, which can continue to learn the procedure of feature...
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An Optimized Bagging Learning with Ensemble Feature Selection Method for URL Phishing Detection
This study proposes and implements an ensemble feature selection with a bagging classifier for URL phishing detection. Feature Selection is essential...
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Intelligent Feature Engineering and Feature Selection Techniques for Machine Learning Evaluation
Manual feature engineering can take a long time and be ineffective at capturing complicated patterns, while choosing the wrong features can produce...
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Reliable feature selection for adversarially robust cyber-attack detection
The growing cybersecurity threats make it essential to use high-quality data to train machine learning (ML) models for network traffic analysis,...