Optimize Selection (Weight-Guided)


Synopsis

Adds iteratively features according to input attribute weights


Description

This operator uses input attribute weights to determine the order of features added to the feature set starting with the feature set containing only the feature with highest weight. The inner operators must provide a performance vector to determine the fitness of the current feature set, e.g. a cross validation of a learning scheme for a wrapper evaluation. Stops if adding the last k features does not increase the performance or if all features were added. The value of k can be set with the parameter generations_without_improval.


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ExampleProcess