Improved Genetic Particle Swarm Optimization and Feature Subset Selection for Extreme Learning Machine
DOI:
https://doi.org/10.26438/ijcse/v6si1.4854Keywords:
Feature Subset Selection Problem, Pattern Classification Problem, Particle Swarm Optimization, Extreme Learning MachineAbstract
Particle Swarm Optimization (PSO) is a heuristic global optimization method, which is most commonly used for feature subset selection problem. However, PSO requires the fixed number of optimal features as an input. It is a very critical task to analyze initially that how many features are relevant and non-redundant present in the given dataset. To solve the said problem this paper has proposed Improved Genetic – PSO (IG-PSO) algorithm for Extreme Learning Machine (ELM) which returns optimal features as well as an optimal number of features. The IG-PSO algorithm is experimented on six benchmarked dataset for handling medical dataset classification which improves the classification accuracy by using optimal features. Also, the simulation results demonstrate that IG-PSO algorithm has the capability to handle optimization, dimensionality reduction and supervised binary classification problems.
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