Optimize Weights (PSO)
(AI Studio Core)
Synopsis
Weight the features with a particle swarm optimization approach.Description
This operator performs the weighting of features with a particle swarm approach.
Input
example set (Data table)This is an example set input port
- input (IOObject)
Output
weights (Attribute Weights)- example set (Data table)
This is an example set output port
performance (Performance Vector)
Parameters
- normalize weightsActivates the normalization of all weights.
- population sizeNumber of individuals per generation.
- maximum number of generationsNumber of generations after which to terminate the algorithm.
- use early stoppingEnables early stopping. If unchecked, always the maximum number of generations is performed.
- generations without improvalStop criterion: Stop after n generations without improval of the performance.
- inertia weightThe (initial) weight for the old weighting.
- local best weightThe weight for the individual's best position during run.
- global best weightThe weight for the population's best position during run.
- dynamic inertia weightIf set to true the inertia weight is improved during run.
- min weightThe lower bound for the weights.
- max weightThe upper bound for the weights.
- use local random seedIndicates if a local random seed should be used.
- local random seedSpecifies the local random seed