List of usage examples for weka.filters UnsupervisedFilter interface-usage
From source file classifier.CustomStringToWordVector.java
/**
* <!-- globalinfo-start --> Converts String attributes into a set of attributes
* representing word occurrence (depending on the tokenizer) information from
* the text contained in the strings. The set of words (attributes) is
* determined by the first batch filtered (typically training data).
* <p/>
From source file cn.edu.xjtu.dbmine.StringToWordVector.java
/**
* <!-- globalinfo-start --> Converts String attributes into a set of attributes
* representing word occurrence (depending on the tokenizer) information from
* the text contained in the strings. The set of words (attributes) is
* determined by the first batch filtered (typically training data).
* <p/>
From source file com.esda.util.StringToWordVector.java
/**
* <!-- globalinfo-start --> Converts String attributes into a set of attributes
* representing word occurrence (depending on the tokenizer) information from
* the text contained in the strings. The set of words (attributes) is
* determined by the first batch filtered (typically training data).
* <p/>
From source file en_deep.mlprocess.manipulation.featmodif.FeatureModifierFilter.java
/**
<!-- globalinfo-start -->
* Converts all nominal attributes into binary numeric attributes. An attribute with k values is transformed
* into k binary attributes if the class is nominal (using the one-attribute-per-value approach).
* Binary attributes are left binary, if option '-A' is not given.
* If the class is numeric, you might want to use the supervised version of this filter.
From source file en_deep.mlprocess.manipulation.featmodif.ReplaceMissing.java
/**
<!-- globalinfo-start -->
* Converts all nominal attributes into binary numeric attributes. An attribute with k values is transformed
* into k binary attributes if the class is nominal (using the one-attribute-per-value approach).
* Binary attributes are left binary, if option '-A' is not given.
* If the class is numeric, you might want to use the supervised version of this filter.
From source file en_deep.mlprocess.manipulation.SetAwareNominalToBinary.java
/**
<!-- globalinfo-start -->
* Converts all nominal attributes into binary numeric attributes. An attribute with k values is transformed
* into k binary attributes if the class is nominal (using the one-attribute-per-value approach).
* Binary attributes are left binary, if option '-A' is not given.
* If the class is numeric, you might want to use the supervised version of this filter.
From source file etc.aloe.filters.AbstractRegexFilter.java
/**
* Abstract class representing a Weka filter that detects occurrences of regular
* expressions in a specific string field.
*
* @author Michael Brooks <mjbrooks@uw.edu>
*/
From source file mao.datamining.RemoveUselessColumnsByMissingValues.java
/**
<!-- globalinfo-start -->
* This filter removes attributes that do not vary at all or that vary too much. All constant attributes are deleted automatically, along with any that exceed the maximum percentage of variance parameter. The maximum variance test is only applied to nominal attributes.
* <p/>
<!-- globalinfo-end -->
*
From source file preprocess.StringToWordVector.java
/**
<!-- globalinfo-start -->
* Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings. The set of words (attributes) is determined by the first batch filtered (typically training data).
* <p/>
<!-- globalinfo-end -->
*
From source file zhaop.textmining.proj.MyStringToWordVector.java
/**
* <!-- globalinfo-start --> Converts String attributes into a set of attributes
* representing word occurrence (depending on the tokenizer) information from
* the text contained in the strings. The set of words (attributes) is
* determined by the first batch filtered (typically training data).
* <p/>