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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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Coupling learning for feature selection in categorical data
Feature selection, which is a commonly used data prepossessing technique, focuses on improving model performance and efficiency by removing redundant...
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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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Incremental feature selection based on uncertainty measure for dynamic interval-valued data
Feature selection is an important strategy for knowledge reduction in rough set. Interval-valued data, as an extension of single values, can better...
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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,...
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An evolutionary feature selection method based on probability-based initialized particle swarm optimization
Feature selection is a common data preprocessing technique that aims to construct better models by selecting the most predictive features. Existing...
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Multi-class feature selection via Sparse Softmax with a discriminative regularization
Feature selection plays a critical role in many machine learning applications as it effectively addresses the challenges posed by “the curse of...
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Mixture Kernel-Based Fuzzy-Rough Feature Selection
Fuzzy-rough sets are a hybridisation of fuzzy sets, which encapsulate the related but distinct concepts of fuzziness and indiscernibility in the case...
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Feature selection
Abstract
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Dynamic multi-label feature selection algorithm based on label importance and label correlation
Multi-label distribution is a popular direction in current machine learning research and is relevant to many practical problems. In multi-label...
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Dynamic Feature Selection Based on F-fuzzy Rough Set for Label Distribution Learning
Label distribution learning(LDL) has been received widespread attention as an effective learning paradigm in the field of data mining. However,...
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Deep optimal feature extraction and selection-based motor fault diagnosis using vibration
The rolling elements of the induction motor are highly susceptible to faults. The detection and diagnosis of rolling element faults are accurate and...
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Orca Predator Algorithm for Feature Selection
In the era of data explosion, the volume and dimensionality of information pose significant challenges to the accuracy and effectiveness of machine...
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GNSS jamming detection using attention-based mutual information feature selection
Global navigation satellite systems (GNSS) are extensively utilized for military and civilian applications. Unfortunately, because of the signal...
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A mixture distributions analysis based feature selection approach for bearing remaining useful life estimation
Feature selection is a difficult but highly important preliminary step for bearings remaining useful life (RUL) estimation. To avoid the weights...
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Multi-label feature selection via spectral clustering-based label enhancement and manifold distribution consistency
Multi-label feature selection can effectively improve the performance and efficiency of subsequent learning tasks by selecting important features...
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Feature gene selection based on fuzzy neighborhood joint entropy
This paper addresses the feature selection problem for gene expression profiles. Feature selection based on fuzzy neighborhood rough sets is very...
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Effect of Feature Selection on the Prediction Model of FeO Content in Sinter
The prediction of FeO content in sinter is important for operators to adjust the raw material ratio and process parameters. Feature selection is used...
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Selection of consistent breath biomarkers of abnormal liver function using feature selection: a pilot study
PurposeBreath profiling has gained importance in recent years as it is a non-invasive technique to identify biomarkers for various diseases. Breath...
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Self-adaptive interval dominance-based feature selection for monotonic classification of interval-valued attributes
Dominance rough set theory is a key mathematical tool for addressing monotonic classification tasks (MCTs). However, current dominance rough set...
