Part 1: Simulation, Optimization, Monitoring and Control TechnologyInternational audienceDue to large amount of calculation and slow speed of the feature selection for cotton fiber, a fast feature selection algorithm based on PSO was developed. It is searched by particle swarm optimization algorithm. Though search features by using PSO, it is reduced the number of classifier training and reduced the computational complexity. Experimental results indicate that, in the case of no loss of the classification performances, the method accelerates feature selection
In machine learning, discretization and feature selection (FS) are important techniques for preproce...
Feature selection (FS) is an important and challenging task in machine learning. FS can be defined a...
Recent research has shown that Particle Swarm Optimisation is a promising approach to feature select...
International audienceThe excellent feature set or feature combination of cotton foreign fibers is g...
International audienceFeature selection are highly important to improve the classification accuracy ...
Feature selection (FS) is an important data preprocessing technique, which has two goals of minimisi...
Forming an efficient feature space for classification problems is a grand challenge in pattern recog...
Machine learning has been expansively examined with data classification asthe most popularly researc...
In classification, feature selection is an important, but difficult problem. Particle swarm optimisa...
© 2015 IEEE. Feature selection is an important pre-processing step, which can reduce the dimensional...
In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature sel...
Classification problems often have a large number of features in the data sets, but not all of them ...
Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop...
[[abstract]]Searching for an optimal feature subset in a high-dimensional feature space is an NP-com...
Feature selection can classify the data with irrelevant features and improve the accuracy of data cl...
In machine learning, discretization and feature selection (FS) are important techniques for preproce...
Feature selection (FS) is an important and challenging task in machine learning. FS can be defined a...
Recent research has shown that Particle Swarm Optimisation is a promising approach to feature select...
International audienceThe excellent feature set or feature combination of cotton foreign fibers is g...
International audienceFeature selection are highly important to improve the classification accuracy ...
Feature selection (FS) is an important data preprocessing technique, which has two goals of minimisi...
Forming an efficient feature space for classification problems is a grand challenge in pattern recog...
Machine learning has been expansively examined with data classification asthe most popularly researc...
In classification, feature selection is an important, but difficult problem. Particle swarm optimisa...
© 2015 IEEE. Feature selection is an important pre-processing step, which can reduce the dimensional...
In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature sel...
Classification problems often have a large number of features in the data sets, but not all of them ...
Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop...
[[abstract]]Searching for an optimal feature subset in a high-dimensional feature space is an NP-com...
Feature selection can classify the data with irrelevant features and improve the accuracy of data cl...
In machine learning, discretization and feature selection (FS) are important techniques for preproce...
Feature selection (FS) is an important and challenging task in machine learning. FS can be defined a...
Recent research has shown that Particle Swarm Optimisation is a promising approach to feature select...