Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach

By Bing Xue, Mengjie Zhang, Will N. Browne. IEEE Transactions on Cybernetics, 2013

557 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/a9df26375f2640536bea97b5d486d339e8512061

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yaochu Jin
2. Ran Cheng
3. Xing-yi Zhang
4. Ye Tian
5. Mengjie Zhang
6. Bing Xue
7. Shenkai Gu
8. Jun Zhang

In Computational Theory and Mathematics

1. Yaochu Jin
2. Ran Cheng
3. Xing-yi Zhang
4. Ye Tian
5. Jun Zhang
6. Shenkai Gu
7. Mengjie Zhang
8. Bing Xue

Citations in the corpus

• Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, methodology): “However, canonical PSO has many limitations for feature selection (Xue et al. 2013, 2014).”
• Genetic Learning Particle Swarm Optimization (2016, background): “Owing to its conceptual simplicity and high efficiency, PSO has been successful in solving a variety of problems in many areas such as power systems [3], [4], industrial electronics [5], wireless sensor networks [6], and feature selection [7].”
• Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification (2019, methodology): “PSO has been widely used in feature selection problems (Xue et al. 2013, 2014a).”
• Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms (2014, methodology): “According to our preliminary experiments [52, 53], the threshold θ is set as 0.”
• An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, methodology): “In general, the features in Pareto optimal solutions account for a small portion of all candidate features [8].”
• Binary ant lion approaches for feature selection (2016, background): “In practice, the exhaustive search techniques are impossible to get the optimal solution and still suffer from stagnation in local optima [6].”
• A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition (2017, background): “[8] proposed two PSO-based multiobjective feature selection algorithms, i.”