A distributed PSO-SVM hybrid system with feature selection and parameter optimization

By Cheng-Lung Huang, Jian-Fan Dun. Applied Soft Computing, 2008

573 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/47f311f7d34b63f781adb5116f5d1088dbe89f81

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. Bing Xue
2. Mengjie Zhang
3. Will N. Browne
4. Yaochu Jin
5. Kunlong Yin
6. Xin Yao
7. Abdulhamit Subasi
8. Ran Cheng

Citations in the corpus

• Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013, methodology): “To implement our proposed approach, the RBF kernel function is used for the SVM classifier since the RBF kernel function can analyze higher dimensional data [16,41].”
• Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms (2014, background): “Both of them have been applied in feature selection problems and achieve good results [26, 4, 31].”
• A Survey on Evolutionary Computation Approaches to Feature Selection (2016, methodology): “The typical representation has been slightly modified to simultaneously perform feature selection and parameter optimzation of a classification algorithm, mostly optimizing the parameters in the kernel functions of SVMs [138], [141], [152], [178].”
• Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model (2020, background): “The prediction ability of the model is not determined by the quantity of triggering factors, but by the quality control of it (Deng et al. 2017; Huang and Dun 2008; Zhang et al. 2020a, b, c).”
• Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, unknown): “Although both versions of PSO have been successfully applied to single objective feature selection [3], [9], [10], [12], [13], binary PSO has potential limitations, such as the position of a particle in binary PSO is updated solely based on the velocity while the position in standard PSO is updated…”