Package weka.classifiers.trees
Class UserClassifier
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.trees.UserClassifier
- All Implemented Interfaces:
Serializable
,Cloneable
,CapabilitiesHandler
,Drawable
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
,TreeDisplayListener
,VisualizePanelListener
public class UserClassifier
extends Classifier
implements Drawable, TreeDisplayListener, VisualizePanelListener, TechnicalInformationHandler
Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of the decision tree. You can create binary splits by creating polygons around data plotted on the scatter graph, as well as by allowing another classifier to take over at points in the decision tree should you see fit.
For more information see:
Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud.. 55(3):281-292. BibTeX:
For more information see:
Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud.. 55(3):281-292. BibTeX:
@article{Ware2001, author = {Malcolm Ware and Eibe Frank and Geoffrey Holmes and Mark Hall and Ian H. Witten}, journal = {Int. J. Hum.-Comput. Stud.}, number = {3}, pages = {281-292}, title = {Interactive machine learning: letting users build classifiers}, volume = {55}, year = {2001}, PS = {http://www.cs.waikato.ac.nz/\~ml/publications/2000/00MW-etal-Interactive-ML.ps} }Valid options are:
-D If set, classifier is run in debug mode and may output additional info to the console
- Version:
- $Revision: 5535 $
- Author:
- Malcolm Ware (mfw4@cs.waikato.ac.nz)
- See Also:
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Field Summary
Fields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
Call this function to build a decision tree for the training data provided.double[]
Call this function to get a double array filled with the probability of how likely each class type is the class of the instance.Returns default capabilities of the classifier.Returns the revision string.Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.This will return a string describing the classifier.graph()
Returns a string that describes a graph representing the object.int
Returns the type of graph this classifier represents.static void
Main method for testing this class.toString()
void
Receives user choices from the tree view, and then deals with these choices.void
This receives shapes from the data view.Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions
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Constructor Details
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UserClassifier
public UserClassifier()Constructor
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Method Details
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main
Main method for testing this class.- Parameters:
argv
- should contain command line options (see setOptions)
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toString
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userCommand
Receives user choices from the tree view, and then deals with these choices.- Specified by:
userCommand
in interfaceTreeDisplayListener
- Parameters:
e
- The choice.
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userDataEvent
This receives shapes from the data view. It then enters these shapes into the decision tree structure.- Specified by:
userDataEvent
in interfaceVisualizePanelListener
- Parameters:
e
- Contains the shapes, and other info.
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graphType
public int graphType()Returns the type of graph this classifier represents. -
graph
Description copied from interface:Drawable
Returns a string that describes a graph representing the object. The string should be in XMLBIF ver. 0.3 format if the graph is a BayesNet, otherwise it should be in dotty format. -
globalInfo
This will return a string describing the classifier.- Returns:
- The string.
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getTechnicalInformation
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
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buildClassifier
Call this function to build a decision tree for the training data provided.- Specified by:
buildClassifier
in classClassifier
- Parameters:
i
- The training data.- Throws:
Exception
- if can't build classification properly.
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distributionForInstance
Call this function to get a double array filled with the probability of how likely each class type is the class of the instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
i
- The instance to classify.- Returns:
- A double array filled with the probalities of each class type.
- Throws:
Exception
- if can't classify instance.
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getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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