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Optimize Weights (Backward)

Synopsis​

Assumes that features are independent and optimizes the weights of the attributes with a linear search.

Description​

Uses the backward selection idea for the weighting of features.

Input​

example set​

This is an example set input port

through​

through input port, that leaves the content untouched.

Output​

example set​

This is an example set output port

weights​

performance​

Parameters​

keep best​

Keep the best n individuals in each generation.

generations without improval​

Stop after n generations without improvement of the performance.

weights​

Use these weights for the creation of individuals in each generation.

normalize weights​

Indicates if the final weights should be normalized.

use local random seed​

Indicates if a local random seed should be used.

local random seed​

Specifies the local random seed

user result individual selection​

Determines if the user wants to select the final result individual from the last population.

show population plotter​

Determines if the current population should be displayed in performance space.

plot generations​

Update the population plotter in these generations.

constraint draw range​

Determines if the draw range of the population plotter should be constrained between 0 and 1.

draw dominated points​

Determines if only points which are not Pareto dominated should be painted.

population criteria data file​

The path to the file in which the criteria data of the final population should be saved.

maximal fitness​

The optimization will stop if the fitness reaches the defined maximum.