keel.Algorithms.ImbalancedClassification.Ensembles.C45

Class Tree

java.lang.Object

keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree

public class Tree
extends java.lang.Object

Class to handle the classifier tree

Field Summary

Fields

Modifier and Type	Field and Description
protected float	confidence The confidence factor for pruning.
protected boolean	isEmpty Is this node empty or not.
protected boolean	isLeaf Is this node leaf or not.
protected SelectCut	model The selected model.
protected Cut	nodeModel The model of the node.
static int	NumberOfLeafs Number of Leafs in the tree
static int	NumberOfNodes Total number of Nodes in the tree
protected boolean	prune Is pruned the tree or not.
protected Tree[]	sons Sons of the node.
protected Dataset	train The dataset.

Constructor Summary

Constructors

Constructor and Description
Tree(SelectCut selectNodeModel, boolean pruneTree, float cf) Constructor.

Method Summary

Modifier and Type	Method and Description
void	buildNode(Dataset data) Adds one new node.
void	buildTree(Dataset data) Function to build the classifier tree.
double[]	classificationForItemset(Itemset itemset) Function to get the classification of classes.
void	collapse() Function to collapse a tree to a node if training error doesn't increase.
protected Tree	getNewTree(Dataset data) Function to create a new tree.
void	prune() Function to prune a tree.
java.lang.String	toString() Function to print the tree.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

NumberOfNodes

public static int NumberOfNodes

Total number of Nodes in the tree

NumberOfLeafs

public static int NumberOfLeafs

Number of Leafs in the tree

model

protected SelectCut model

The selected model.

nodeModel

protected Cut nodeModel

The model of the node.

sons

protected Tree[] sons

Sons of the node.

isLeaf

protected boolean isLeaf

Is this node leaf or not.

isEmpty

protected boolean isEmpty

Is this node empty or not.

train

protected Dataset train

The dataset.

prune

protected boolean prune

Is pruned the tree or not.

confidence

protected float confidence

The confidence factor for pruning.

Constructor Detail

Tree

public Tree(SelectCut selectNodeModel,
            boolean pruneTree,
            float cf)

Constructor.

Parameters:

selectNodeModel - The cut model.

pruneTree - Prune the tree or not.

cf - Minimum confidence.

Method Detail

buildNode

public void buildNode(Dataset data)
               throws java.lang.Exception

Adds one new node.

Parameters:

data - The dataset.

Throws:

java.lang.Exception - If the node cannot be built.

buildTree

public void buildTree(Dataset data)
               throws java.lang.Exception

Function to build the classifier tree.

Parameters:

data - The dataset.

Throws:

java.lang.Exception - If the tree cannot be built.

collapse

public final void collapse()

Function to collapse a tree to a node if training error doesn't increase.

prune

public void prune()
           throws java.lang.Exception

Function to prune a tree.

Throws:

java.lang.Exception - If the prune cannot be made.

classificationForItemset

public final double[] classificationForItemset(Itemset itemset)
                                        throws java.lang.Exception

Function to get the classification of classes.

Parameters:

itemset - The itemset to classify.

Returns:

The classification of class values for the itemset.

Throws:

java.lang.Exception - If the probabilities cannot be computed.

toString

public java.lang.String toString()

Function to print the tree.

Overrides:

toString in class java.lang.Object

Returns:

String representation of the tree

getNewTree

protected Tree getNewTree(Dataset data)
                   throws java.lang.Exception

Function to create a new tree.

Parameters:

data - The dataset.

Returns:

The new tree.

Throws:

java.lang.Exception - If the new tree cannot be created.