A Feature Selection Method Using Conditional Correlation Dispersion and Redundancy Analysis

Li, Zhang

doi:10.1007/s11063-023-11256-7

Neural Process Lett

2023

DOI: 10.1007/s11063-023-11256-7

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A Feature Selection Method Using Conditional Correlation Dispersion and Redundancy Analysis

Zhang Li

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2024

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Cited by 6 publications

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Feature subset selection through nature inspired computing for efficient glaucoma classification from fundus images

Singh,

Khanna,

Singh

2024

Multimed Tools Appl

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Feature subset selection through nature inspired computing for efficient glaucoma classification from fundus images

Singh,

Khanna,

Singh

2024

Multimed Tools Appl

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A Correlation-Redundancy Guided Evolutionary Algorithm and Its Application to High-Dimensional Feature Selection in Classification

Sun,

Guo,

Liu

et al. 2024

Neural Process Lett

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The processing of high-dimensional datasets has become unavoidable with the development of information technology. Most of the literature on feature selection (FS) of high-dimensional datasets focuses on improvements in search strategies, ignoring the characteristics of the dataset itself such as the correlation and redundancy of each feature. This could degrade the algorithm's search effectiveness. Thus, this paper proposes a correlation-redundancy guided evolutionary algorithm (CRGEA) to address high-dimensional FS with the objectives of optimizing classification accuracy and the number of features simultaneously. A new correlation-redundancy assessment method is designed for selecting features with high relevance and low redundancy to speed up the entire evolutionary process. In CRGEA, a novel initialization strategy combined with a multiple threshold selection mechanism is developed to produce a high-quality initial population. A local acceleration evolution strategy based on a parallel simulated annealing algorithm and a pruning method is developed, which can search in different directions and perform deep searches combing the annealing stage around the best solutions to improve the local search ability. Finally, the comparison experiments on 16 public high-dimensional datasets verify that the designed CRGEA outperforms other state-of-the-art intelligent algorithms. The CRGEA can efficiently reduce redundant features while ensuring high accuracy.

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A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm

Alirezapour,

Mansouri,

Mohammad Hasani Zade

2024

Neural Process Lett

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Recent growth in data dimensions presents challenges to data mining and machine learning. A high-dimensional dataset consists of several features. Data may include irrelevant or additional features. By removing these redundant and unwanted features, the dimensions of the data can be reduced. The feature selection process eliminates a small set of relevant and important features from a large data set, reducing the size of the dataset. Multiple optimization problems can be solved using metaheuristic algorithms. Recently, the Grasshopper Optimization Algorithm (GOA) has attracted the attention of researchers as a swarm intelligence algorithm based on metaheuristics. An extensive review of papers on GOA-based feature selection algorithms in the years 2018–2023 is presented based on extensive research in the area of feature selection and GOA. A comparison of GOA-based feature selection methods is presented, along with evaluation strategies and simulation environments in this paper. Furthermore, this study summarizes and classifies GOA in several areas. Although many researchers have introduced their novelty in the feature selection problem, many open challenges and enhancements remain. The survey concludes with a discussion about some open research challenges and problems that require further attention.

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A Feature Selection Method Using Conditional Correlation Dispersion and Redundancy Analysis

Cited by 6 publications

References 45 publications

Feature subset selection through nature inspired computing for efficient glaucoma classification from fundus images

Feature subset selection through nature inspired computing for efficient glaucoma classification from fundus images

A Correlation-Redundancy Guided Evolutionary Algorithm and Its Application to High-Dimensional Feature Selection in Classification

A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm

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