(AI Studio Core)
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 (IOObject)This is an example set input port
- through (IOObject)
through input port, that leaves the content untouched.
Output
- example set (Data Table)
This is an example set output port
weights (Attribute Weights)
performance (Performance Vector)
Parameters
- keep bestKeep the best n individuals in each generation.
- generations without improvalStop after n generations without improvement of the performance.
- weightsUse these weights for the creation of individuals in each generation.
- normalize weightsIndicates if the final weights should be normalized.
- use local random seedIndicates if a local random seed should be used.
- local random seedSpecifies the local random seed
- user result individual selectionDetermines if the user wants to select the final result individual from the last population.
- show population plotterDetermines if the current population should be displayed in performance space.
- plot generationsUpdate the population plotter in these generations.
- constraint draw rangeDetermines if the draw range of the population plotter should be constrained between 0 and 1.
- draw dominated pointsDetermines if only points which are not Pareto dominated should be painted.
- population criteria data fileThe path to the file in which the criteria data of the final population should be saved.
- maximal fitnessThe optimization will stop if the fitness reaches the defined maximum.