A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy

By Parham Moradi, Mozhgan Gholampour. Applied Soft Computing, 2016

361 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9fc78e0c52e8104623b54d29d3c98386846347ac

How researchers see this paper

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

1. Ali Asghar Heidari
2. Hossam Faris
3. Ibrahim Aljarah
4. Ala’ M. Al-Zoubi
5. Qasem Al-Tashi
6. Hamido Fujita
7. Said Jadid Abdul Kadir
8. Helmi Md Rais

Citations in the corpus

• An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “By selecting the most informative feature and removing the irrelevant redundant features, the dimensionality of the feature space will be reduced and the convergence speed of the learning algorithm will be improved [30].”
• Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection (2019, methodology): “Moreover, a hybrid PSO with novel local search strategy based on information correlation was proposed in [36].”