Public

<init>

Searches the attribute subset space by forward selection.

search

Records whether the user has requested a ranked list of attributes.

setGenerateRanking

Set whether to search backwards instead of forwards

setSearchBackwards

converts a BitSet into a list of attribute indexes

attributeList

determineNumToSelectFromThreshold

Gets whether conservative selection has been enabled

getConservativeForwardSelection

Get whether to output debugging info to the console

getDebuggingOutput

Gets whether ranking has been requested. This is used by the AttributeSelection
module to determine 

getGenerateRanking

getNumExecutionSlots

Gets the number of attributes to be retained.

getNumToSelect

getSearchBackwards

Returns a list of attributes (and or attribute ranges) as a String

getStartSet

Returns the threshold so that the AttributeSelection module can discard
attributes from the ranking.

getThreshold

numExecutionSlotsTipText

resetOptions

Set whether attributes should continue to be added during a forward search as
long as merit does not

setConservativeForwardSelection

Set whether to output debugging info to the console

setDebuggingOutput

setNumExecutionSlots

Specify the number of attributes to select from the ranked list (if generating a
ranking). -1 indica

setNumToSelect

FileOutputStream

Running tasks concurrently on multiple threads

A point representing a location in (x,y) coordinate space, specified in integer
precision.

java.awt.Point

Fake signature of an existing Java class.

java.io.PrintStream

A legacy iteration interface.New code should use Iterator instead. Iterator
replaces the enumeration

java.util.Enumeration

A Properties object is a Hashtable where the keys and values must be Strings.
Each property can have

java.util.Properties

Vector is an implementation of List, backed by an array and synchronized. All
optional operations in

java.util.Vector

Defines the contract between a returned instance and the runtime when an
application needs to provid

javax.ws.rs.core.Response

Return the contained value, if present, otherwise throw an exception to be
created by the provided s

orElseThrow

setRequestProperty

scheduleAtFixedRate

/**
   * Produces a ranked list of attributes. Search must have been performed prior
   * to calling this function. Search is called by this function to complete the
   * traversal of the the search space. A list of attributes and merits are
   * returned. The attributes a ranked by the order they are added to the subset
   * during a forward selection search. Individual merit values reflect the
   * merit associated with adding the corresponding attribute to the subset;
   * because of this, merit values may initially increase but then decrease as
   * the best subset is "passed by" on the way to the far side of the search
   * space.
   * 
   * @return an array of attribute indexes and associated merit values
   * @throws Exception if something goes wrong.
   */

"Search must be performed before attributes "

"More attributes requested than exist in the data"

/**
   * Ranks attributes using the specified attribute evaluator and then searches
   * the ranking using the supplied subset evaluator.
   * 
   * @param ASEval the subset evaluator to guide the search
   * @param data the training instances.
   * @return an array (not necessarily ordered) of selected attribute indexes
   * @throws Exception if the search can't be completed
   */

/*
     * if (m_ASEval instanceof AttributeTransformer) { throw new
     * Exception("Can't use an attribute transformer " +"with RankSearch"); }
     */

/*
     * if (m_ASEval instanceof UnsupervisedAttributeEvaluator || NOT USED
     * m_ASEval instanceof UnsupervisedSubsetEvaluator) { m_hasClass = false;
     */

/*
     * if (!(m_SubsetEval instanceof UnsupervisedSubsetEvaluator)) { throw new
     * Exception("Must use an unsupervised subset evaluator."); }
     */

/*
     * } NOT USED else { m_hasClass = true; m_classIndex =
     * m_Instances.classIndex(); }
     */

// get the transformed data a rebuild the subset evaluator

How to use searchmethodin weka.attributeSelection.GreedyStepwise

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How to use
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