Journal ArticleDOI
Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost
Ying Hu,Yong Zhang,Dunwei Gong +2 more
TLDR
A fuzzy multiobjective FS method with particle swarm optimization, called PSOMOFS, is studied, which develops a fuzzy dominance relationship to compare the goodness of candidate particles and defines a fuzzy crowding distance measure to prune the elitist archive and determine the global leader of particles.Abstract:
Feature selection (FS) is an important data processing technique in the field of machine learning. There have been various FS methods, but all assume that the cost associated with a feature is precise, which restricts their real applications. Focusing on the FS problem with fuzzy cost, a fuzzy multiobjective FS method with particle swarm optimization, called PSOMOFS, is studied in this article. The proposed method develops a fuzzy dominance relationship to compare the goodness of candidate particles and defines a fuzzy crowding distance measure to prune the elitist archive and determine the global leader of particles. Also, a tolerance coefficient is introduced into the proposed method to ensure that the Pareto-optimal solutions obtained satisfy decision makers’ preferences. The developed method is used to tackle a series of the UCI datasets and is compared with three fuzzy multiobjective evolutionary methods and three typical multiobjective FS methods. Experimental results show that the proposed method can achieve feature sets with superior performances in approximation, diversity, and feature cost.read more
Citations
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Journal ArticleDOI
A comprehensive survey on recent metaheuristics for feature selection
TL;DR: The most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques are surveyed in this paper .
Journal ArticleDOI
bSSA: Binary Salp Swarm Algorithm With Hybrid Data Transformation for Feature Selection
TL;DR: In this article, a new binary Salp Swarm Algorithm (bSSA) was proposed for selecting the best feature set from transformed datasets, which first transforms the original data-set using Principal Component Analysis (PCA) and fast independent component analysis (fastICA) based hybrid data transformation methods; next, a binary salp swarm optimizer is used for finding the best features.
Journal ArticleDOI
Multi-Objective Feature Selection With Missing Data in Classification
TL;DR: A novel modelling of FS is proposed that includes reliability as the third objective of the problem and the application of the non-dominated sorting genetic algorithm-III (NSGA-III) is proposed to address the modified problem.
Journal ArticleDOI
Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model
Ahmed A. Ewees,Mohammed A. A. Al-qaness,Laith Abualigah,Diego Oliva,Zakariya Yahya Algamal,Ahmed M. Anter,Rehab Ali Ibrahim,Rania M. Ghoniem,Mohamed Abd Elaziz +8 more
TL;DR: The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.
Journal ArticleDOI
Multi-objective PSO based online feature selection for multi-label classification
TL;DR: The proposed algorithm outperforms the results obtained by state-of-the-art approaches in most cases and automatically determines the best subset of features that is suitable for multi-label classification.
References
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Wrappers for feature subset selection
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MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
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Journal ArticleDOI
A Survey on Evolutionary Computation Approaches to Feature Selection
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