A Survey on Evolutionary Computation Approaches to Feature Selection

By Bing Xue, Mengjie Zhang, Will N. Browne, Xin Yao. 2016

1,551 citations recorded by the source corpus.

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

Subfields: Artificial Intelligence

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Citations in the corpus

Population Based Training of Neural Networks (2017, methodology): “This mixture of learning and evolutionary-like algorithms has also been explored successfully in other domains (Zhang et al., 2011) such as neural architecture search (Real et al., 2017; Liu et al., 2017), feature selection (Xue et al., 2016), and parameter learning (Fernando et al., 2016).”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, methodology): “In particular, PSO, as a popular metaheuristics, has also been widely adopted for feature selection (Xue et al. 2016).”
Spectral features extraction for estimation of soil total nitrogen content based on modified ant colony optimization algorithm (2019, background): “It is especially suitable for the relatively small-scale problems (Xue et al., 2016).”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “In [13], Xue et al. provided a comprehensive survey of Evolutionary Computation (EC) techniques for FS.”
Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “[1] investigated that on medium-size datasets of features SVM algorithm was adopted to present the prominent features with the time constraints.”
Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification (2019, methodology): “Based on evaluation methods, feature selection methods can be simply classified into two categories: filter and wrapper (Xue et al. 2016).”
Differential evolution for filter feature selection based on information theory and feature ranking (2018, methodology): “Furthermore, although feature selection can be considered as a multi-objective problem, there are only a few works on multi-objective ﬁlter feature selection [5,23] .”
A comprehensive survey on recent metaheuristics for feature selection (2022, background): “indicated that the feature selection problem is very hard due to its complex search space [70].”
Bio-inspired computation: Where we stand and what's next (2019, background): “Besides other multiple scenarios leveraging this profitable complementarity (such as feature selection/construction [435], oppositionbased learning for bio-inspired optimization methods [436] or their hybridization with elements from Reinforcement Learning [437]), possibilities for the future are fo”