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 and 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 expression Valid options are:
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	PKIDiscretize Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values. 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).