Class UserClassifier

java.lang.Object
weka.classifiers.Classifier
weka.classifiers.trees.UserClassifier
All Implemented Interfaces:
Serializable, Cloneable, CapabilitiesHandler, Drawable, OptionHandler, RevisionHandler, TechnicalInformationHandler, TreeDisplayListener, VisualizePanelListener

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:
  • Constructor Details

    • UserClassifier

      public UserClassifier()
      Constructor
  • Method Details

    • main

      public static void main(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - should contain command line options (see setOptions)
    • toString

      public String toString()
      Overrides:
      toString in class 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 String graph() throws 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:
      Exception - if String can't be built properly.
    • globalInfo

      public 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:
    • buildClassifier

      public void buildClassifier(Instances i) throws 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:
      Exception - if can't build classification properly.
    • distributionForInstance

      public double[] distributionForInstance(Instance i) throws 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:
      Exception - if can't classify instance.
    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class Classifier
      Returns:
      the revision