Uses of Class
weka.filters.SimpleFilter

Packages that use SimpleFilter

weka.filters
weka.filters.supervised.attribute
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance

Uses of SimpleFilter in weka.filters

Subclasses of SimpleFilter in weka.filters

class	MultiFilter Applies several filters successively.
class	SimpleBatchFilter This filter is a superclass for simple batch filters.
class	SimpleStreamFilter This filter is a superclass for simple stream filters.

Uses of SimpleFilter in weka.filters.supervised.attribute

Subclasses of SimpleFilter in weka.filters.supervised.attribute

class	AddClassification A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
class	PLSFilter Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction. By default it replaces missing values and centers the data. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).

Uses of SimpleFilter in weka.filters.unsupervised.attribute

Subclasses of SimpleFilter in weka.filters.unsupervised.attribute

class	ClassAssigner Filter that can set and unset the class index.
class	InterquartileRange A filter for detecting outliers and extreme values based on interquartile ranges.
class	KernelFilter Converts the given set of predictor variables into a kernel matrix.
class	NumericCleaner A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.
class	NumericToNominal A filter for turning numeric attributes into nominal ones.
class	PartitionedMultiFilter A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
class	RandomSubset Chooses a random subset of attributes, either an absolute number or a percentage.
class	RELAGGS A propositionalization filter inspired by the RELAGGS algorithm. It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output).
class	Wavelet A filter for wavelet transformation. For more information see: Wikipedia (2004).

Uses of SimpleFilter in weka.filters.unsupervised.instance

Subclasses of SimpleFilter in weka.filters.unsupervised.instance

class

SubsetByExpression Filters instances according to a user-specified expression. Grammar: boolexpr_list ::= boolexpr_list boolexpr_part | boolexpr_part; boolexpr_part ::= boolexpr:e {: parser.setResult(e); :} ; boolexpr ::= BOOLEAN | true | false | expr < expr | expr <= expr | expr > expr | expr >= expr | expr = expr | ( boolexpr ) | not boolexpr | boolexpr and boolexpr | boolexpr or boolexpr | ATTRIBUTE is STRING ; expr ::= NUMBER | ATTRIBUTE | ( expr ) | opexpr | funcexpr ; opexpr ::= expr + expr | expr - expr | expr * expr | expr / expr ; funcexpr ::= abs ( expr ) | sqrt ( expr ) | log ( expr ) | exp ( expr ) | sin ( expr ) | cos ( expr ) | tan ( expr ) | rint ( expr ) | floor ( expr ) | pow ( expr for base , expr for exponent ) | ceil ( expr ) ; Notes: - NUMBER any integer or floating point number (but not in scientific notation!) - STRING any string surrounded by single quotes; the string may not contain a single quote though. - ATTRIBUTE the following placeholders are recognized for attribute values: - CLASS for the class value in case a class attribute is set. - ATTxyz with xyz a number from 1 to # of attributes in the dataset, representing the value of indexed attribute. Examples: - extracting only mammals and birds from the 'zoo' UCI dataset: (CLASS is 'mammal') or (CLASS is 'bird') - extracting only animals with at least 2 legs from the 'zoo' UCI dataset: (ATT14 >= 2) - extracting only instances with non-missing 'wage-increase-second-year' from the 'labor' UCI dataset: not ismissing(ATT3) Valid options are: