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A Feature Selection Method Based on Feature-Label Correlation Information and Self-Adaptive MOPSO
Feature selection can be seen as a multi-objective task, where the goal is to select a subset of features that exhibit minimal correlation among...
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A parallel feature selection method based on NMI-XGBoost and distance correlation for typhoon trajectory prediction
Typhoon trajectory related data involve many factors, such as atmospheric factors, oceanic factors, and physical factors. It has the characteristics...
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Improved Filter-Based Feature Selection Using Correlation and Clustering Techniques
Feature engineering and feature selection are essential techniques to most data science and machine learning applications, in which, respectively,...
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Sparse feature selection via local feature and high-order label correlation
Recently, some existing feature selection approaches neglect the correlation among labels, and almost manifold-based multilabel learning models do...
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Multi-objective Optimization Based Feature Selection Using Correlation
The optimal feature selection (FS) problem is widely targeted in the field of machine learning (ML). There are several ways to select the best...
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Feature selection based on mutual information with correlation coefficient
Feature selection is an important preprocessing process in machine learning. It selects the crucial features by removing irrelevant features or...
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Implementation of an integrated classification approach of adaptive extreme learning machine and correlation based feature selection for odia complex characters
Handwritten character recognition is one of the most explored branch of optical Character Recognition in the field of research and development for...
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Label distribution feature selection based on label-specific features
Label distribution learning, where deal with label ambiguity by describing the degree of relevance of each label to a specific instance. As a novel...
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GAAMmf: genetic algorithm with aggressive mutation and decreasing feature set for feature selection
This paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM), one of the genetic algorithms (GAs) used for feature...
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Feature Selection
Feature selection is a class of dimensionality reduction in which an “im- portant” subset of features from a larger set of features is selected. A...
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Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning
Internet of Things (IoT) devices are widely used but also vulnerable to cyberattacks that can cause security issues. To protect against this, machine...
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Joint subspace reconstruction and label correlation for multi-label feature selection
High-dimensional multi-label data has become more prevalent in many application domains, presenting difficulties and challenges for multi-label...
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Three-phases hybrid feature selection for facial expression recognition
Machine learning applications are increasingly challenged by the growing volume of data. In this context, selecting relevant features from the vast...
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Machine learning-based intrusion detection: feature selection versus feature extraction
Internet of Things (IoTs) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart...
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Multi-label feature selection via redundancy of the selected feature set
Due to the growing number of high-dimensional multi-label data which emerge in modern applications, multi-label feature selection becomes an...
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Alternative feature selection with user control
Feature selection is popular for obtaining small, interpretable, yet highly accurate prediction models. Conventional feature-selection methods...
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Feature Importance and Selection
This chapter offers an in-depth exploration of various methods used to assess feature importance in machine learning models. Initially, it highlights...
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Correlation-based advanced feature analysis for wireless sensor networks
In this study, we focus on real-time anomaly detection using the gated graph neural network (GGNN) and long short-term memory (LSTM) algorithms for...
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A new feature selection algorithm based on fuzzy-pathfinder optimization
Data mining and machine learning require feature selection because features can dramatically improve model performance. In contrast, there are no...
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Feature Selection on Inconsistent Data
With the explosive growth of data size, inconsistent data appear more frequently. Due to inconsistent data detection and repairing in data...