|
Class Summary |
| AbstractFeatureSelection |
Abstract superclass of all feature processing operators who remove features
from the example set. |
| AbstractGeneticAlgorithm |
Genetic algorithms are general purpose optimization / search algorithms that are suitable in case of no or little
problem knowledge. |
| AttributeWeightSelection |
This operator selects all attributes which have a weight satisfying a given
condition. |
| BackwardAttributeEliminationOperator |
This operator starts with the full set of attributes and, in each round, it removes each remaining attribute of the
given set of examples. |
| BackwardElimination |
This PopulationOperator realizes backward elimination, i.e. creates a list of
clones of each individual and switches of one attribute in each of the
clones. |
| BestSelection |
Selects the best individual and build a new population. |
| BoltzmannSelection |
Like RouletteWheel this population operator selects a given fixed number of
individuals by subdividing a roulette wheel in sections of size proportional
to the individuals' fitness values. |
| BruteForceSelection |
This feature selection operator selects the best attribute set by trying all
possible combinations of attribute selections. |
| CutSelection |
Creates a new population by a deterministical selection of the best
individuals. |
| FeatureSelectionOperator |
This operator realizes the two deterministic greedy feature selection
algorithms forward selection and backward elimination. |
| ForwardAttributeSelectionOperator |
This operator starts with an empty selection of attributes and, in each round, it adds each unused attribute of the
given set of examples. |
| ForwardSelection |
This PopulationOperator realises forward selection, i.e. creates a list of
clones of each individual and switches on one attribute in each of the
clones. |
| ForwardSelectionOperator |
Deprecated. |
| GeneticAlgorithm |
A genetic algorithm for feature selection (mutation=switch features on and off, crossover=interchange used features). |
| IterativeFeatureAdding |
Adds iteratively the next feature according to given attribute name array. |
| NonDominatedSortingSelection |
Performs the non dominated sorting selection from NSGA II. |
| RandomSelection |
This operator selects a randomly chosen number of attributes randomly from the input example set. |
| RankSelection |
Selects a given fixed number of individuals by subdividing a roulette wheel
in sections of size proportional to the individuals' rank based on their
fitness values. |
| RemoveCorrelatedFeatures |
Removes (un-) correlated features due to the selected filter relation. |
| RemoveUselessFeatures |
Removes useless attribute from the example set. |
| RouletteWheel |
Selects a given fixed number of individuals by subdividing a roulette wheel
in sections of size proportional to the individuals' fitness values. |
| SaveIntermediateWeights |
This population operator writes the currently best weights into the specified file. |
| SelectionCrossover |
Crossover operator for the used bitlists of example sets. |
| SelectionMutation |
Inverts the used bit for every feature of every example set with a given
fixed probability. |
| StochasticUniversalSampling |
Similar to a the roulette wheel selection the fitness values of all
individuals build a partition of the 360 degrees of a wheel. |
| SwitchingForwardSelection |
This PopulationOperator realises forward selection, i.e. creates a list of
clones of each individual and switches on one attribute in each of the
clones. |
| TournamentSelection |
Performs tournaments with k participants. |
| UniformSelection |
Selects a given fixed number of individuals by uniformly sampling from the
current population until the desired population size is reached. |
| WeightGuidedSelectionOperator |
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. |