Uses of Class
weka.filters.unsupervised.attribute.PotentialClassIgnorer

Packages that use PotentialClassIgnorer

Package	Description
weka.filters.unsupervised.attribute

Uses of PotentialClassIgnorer in weka.filters.unsupervised.attribute

Subclasses of PotentialClassIgnorer in weka.filters.unsupervised.attribute

Modifier and Type	Class	Description
class	Center	Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
class	Discretize	An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
class	MathExpression	Modify numeric attributes according to a given mathematical expression.
class	MergeManyValues	Merges many values of a nominal attribute into one value.
class	Normalize	Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
class	NumericToBinary	Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
class	OrdinalToNumeric	An attribute filter that converts ordinal nominal attributes into numeric ones Valid options are:
class	PKIDiscretize	Discretizes numeric attributes using equal frequency binning and forces the number of bins to be equal to the square root of the number of values of the numeric attribute. For more information, see: Ying Yang, Geoffrey I.
class	ReplaceMissingValues	Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
class	ReplaceMissingWithUserConstant	Replaces all missing values for nominal, string, numeric and date attributes in the dataset with user-supplied constant values.
class	Standardize	Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).