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Optimize Weights (PSO)

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​

This is an example set input port

input​

Output​

weights​

example set​

This is an example set output port

performance​

Parameters​

normalize weights​

Activates the normalization of all weights.

population size​

Number of individuals per generation.

maximum number of generations​

Number of generations after which to terminate the algorithm.

use early stopping​

Enables early stopping. If unchecked, always the maximum number of generations is performed.

generations without improval​

Stop criterion: Stop after n generations without improval of the performance.

inertia weight​

The (initial) weight for the old weighting.

local best weight​

The weight for the individual's best position during run.

global best weight​

The weight for the population's best position during run.

dynamic inertia weight​

If set to true the inertia weight is improved during run.

min weight​

The lower bound for the weights.

max weight​

The upper bound for the weights.

use local random seed​

Indicates if a local random seed should be used.

local random seed​

Specifies the local random seed