weka.classifiers.trees
Class UserClassifier

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.trees.UserClassifier

All Implemented Interfaces:

java.io.Serializable, java.lang.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:

 @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:

Serialized Form

Field Summary

Fields inherited from interface weka.core.Drawable

BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

UserClassifier()
Constructor

Method Summary

void	buildClassifier(Instances i) Call this function to build a decision tree for the training data provided.
double[]	distributionForInstance(Instance i) Call this function to get a double array filled with the probability of how likely each class type is the class of the instance.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
java.lang.String	getRevision() Returns the revision string.
TechnicalInformation	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.
java.lang.String	globalInfo() This will return a string describing the classifier.
java.lang.String	graph() Returns a string that describes a graph representing the object.
int	graphType() Returns the type of graph this classifier represents.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	toString()
void	userCommand(TreeDisplayEvent e) Receives user choices from the tree view, and then deals with these choices.
void	userDataEvent(VisualizePanelEvent e) This receives shapes from the data view.

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

UserClassifier

public UserClassifier()

Constructor

Method Detail

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain command line options (see setOptions)

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

Returns:

a string that represents this objects tree.

userCommand

public void userCommand(TreeDisplayEvent e)

Receives user choices from the tree view, and then deals with these choices.

Specified by:

userCommand in interface TreeDisplayListener

Parameters:

e - The choice.

userDataEvent

public void userDataEvent(VisualizePanelEvent e)

This receives shapes from the data view. It then enters these shapes into the decision tree structure.

Specified by:

userDataEvent in interface VisualizePanelListener

Parameters:

e - Contains the shapes, and other info.

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

Drawable.TREE

graph

public java.lang.String graph()
                       throws java.lang.Exception

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.

Specified by:

graph in interface Drawable

Returns:

A string formatted with a dotty representation of the decision tree.

Throws:

java.lang.Exception - if String can't be built properly.

globalInfo

public java.lang.String globalInfo()

This will return a string describing the classifier.

Returns:

The string.

getTechnicalInformation

public TechnicalInformation 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 interface TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class Classifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances i)
                     throws java.lang.Exception

Call this function to build a decision tree for the training data provided.

Specified by:

buildClassifier in class Classifier

Parameters:

i - The training data.

Throws:

java.lang.Exception - if can't build classification properly.

distributionForInstance

public double[] distributionForInstance(Instance i)
                                 throws java.lang.Exception

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 class Classifier

Parameters:

i - The instance to classify.

Returns:

A double array filled with the probalities of each class type.

Throws:

java.lang.Exception - if can't classify instance.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Classifier

Returns:

the revision