Optimize Weights (Forward)
Synopsis
This operator calculates the relevance of the attributes of the given ExampleSet by calculating the attribute weights. This operator assumes that the attributes are independent and optimizes the weights of the attributes with a linear search.
Description
The Optimize Weights (Forward) operator is a nested operator i.e. it has a subprocess. The subprocess of the Optimize Weights (Forward) operator must always return a performance vector. For more information regarding subprocesses please study the Subprocess operator. The Optimize Weights (Forward) operator calculates the weights of the attributes of the given ExampleSet by using the performance vector returned by the subprocess. The higher the weight of an attribute, the more relevant it is considered.
This operator performs the weighting under the naive assumption that the features are independent from each other. Each attribute is weighted with a linear search. This approach may deliver good results after short time if the features indeed are not highly correlated.
Differentiation
Optimize Weights (Evolutionary)
The Optimize Weights (Evolutionary) operator calculates the relevance of the attributes of the given ExampleSet by using an evolutionary approach. The weights of the attributes are calculated using a Genetic Algorithm.
Input
example set in
This input port expects an ExampleSet. This ExampleSet is available at the first port of the nested chain (inside the subprocess) for processing in the subprocess.
through
This operator can have multiple through ports. When one input is connected with the through port, another through port becomes available which is ready to accept another input (if any). The order of inputs remains the same. The Object supplied at the first through port of this operator is available at the first through port of the nested chain (inside the subprocess). Do not forget to connect all inputs in correct order. Make sure that you have connected the right number of ports at subprocess level.
Output
example set out
The resultant ExampleSet with reduced attributes is delivered through this port.
weights
The attribute weights are delivered through this port.
performance
This port delivers the Performance Vector for the selected attributes. A Performance Vector is a list of performance criteria values.
file
This port delivers the file in which the criteria data of the final population is saved.
Parameters
keep best
This parameter specifies the number of best individuals to keep in each generation.
generations without improval
This parameter specifies the stop criterion for early stopping i.e. it stops after n generations without improvement in the performance. n is specified by this parameter.
weights
This parameter specifies the weights to be used for the creation of individuals in each generation.
normalize weights
This parameter indicates if the final weights should be normalized. If set to true, the final weights are normalized such that the maximum weight is 1 and the minimum weight is 0.
use local random seed
This parameter indicates if a local random seed should be used for randomization. Using the same value of local random seed will produce the same randomization.
local random seed
This parameter specifies the local random seed. This parameter is available only if the use local random seed parameter is set to true.
user result individual selection
If this parameter is set to true, it allows the user to select the final result individual from the last population.
show population plotter
This parameter determines if the current population should be displayed in the performance space.
plot generations
This parameter is only available when the show population plotter parameter is set to true. The population plotter is updated in these generations.
constraint draw range
This parameter is only available when the show population plotter parameter is set to true. This parameter determines if the draw range of the population plotter should be constrained between 0 and 1.
draw dominated points
This parameter is only available when the show population plotter parameter is set to true. This parameter determines if only points which are not Pareto dominated should be drawn on the population plotter.
maximal fitness
This parameter specifies the maximal fitness. The optimization will stop if the fitness reaches this value.