netkit.classifiers.relational
Class NetworkOnlyBayes

java.lang.Object
  netkit.classifiers.ClassifierImp
      netkit.classifiers.relational.NetworkClassifierImp
          netkit.classifiers.relational.NetworkOnlyBayes

All Implemented Interfaces:

Classifier, NetworkClassifier, Configurable

public final class NetworkOnlyBayes
extends NetworkClassifierImp

Network-only Bayes Classifier induces a naive Bayes model based on labels of neighbors of a node and uses a Markov random field formulation when one or more neighbors have estimated labels. References:

• Soumen Chakrabarti, Byron Dom and Piotr Indyk (1998) Enhanced Hypertext Categorization Using Hyperlinks, SIGMOD, 1998
• Sofus A. Macskassy, Foster Provost (2007). Classification in Networked Data: A toolkit and a univariate case study. Journal of Machine Learning, 8(May):935-983, 2007.

This replicates the network-only bayes as described in the univariate reference paper. This classifier applies Markov Random Field techniques for dealing with uncertainty in neighborhood (and local) attribute values, and can therefore be used with Relaxation Labeling. This is not the case for the Weka bayes classifier, where you would need the (pseudo-)certainty that is given by Iterative classification and Gibbs sampling. NOTE: This currently takes no parameters and builds a model only on the neighborhood class labels. This is equivalent to these parameters:

.aggregation=ClassOnly
.aggregators=??? (there is no current aggregation technique for uncertainy)
.useintrinsic=false

Author:

Sofus A. Macskassy (sofmac@gmail.com)

Nested Class Summary

Nested classes/interfaces inherited from class netkit.classifiers.relational.NetworkClassifierImp

NetworkClassifierImp.Aggregation

Field Summary

Fields inherited from class netkit.classifiers.relational.NetworkClassifierImp

aggFactory, aggregation, aggregators, aggTypes, dynamicAggregators

Fields inherited from class netkit.classifiers.ClassifierImp

attribute, classPrior, clsIdx, graph, keyIndex, logger, nodeType, right, tmpVector, useIntrinsic, vectorClsIdx

Constructor Summary

NetworkOnlyBayes()

Method Summary

void	configure(Configuration config) Configures this classifier.
boolean	doEstimate(Node node, double[] estimation) compute class estimates.
Configuration	getDefaultConfiguration() Create a default configuration for this classifier.
java.lang.String	getDescription()
java.lang.String	getName()
java.lang.String	getShortName()
void	induceModel(Graph graph, DataSplit split) Induce the model by computing the counts for Prob(classIdx | neighborClassIdx)
void	reset() Resets internal variables.
java.lang.String	toString()

Methods inherited from class netkit.classifiers.relational.NetworkClassifierImp

classify, estimate, estimate, estimate, estimate, generateAggregators, getAttributeNames, includeClassAttribute, initializeRun, makeVector

Methods inherited from class netkit.classifiers.ClassifierImp

addListener, classify, classify, clearListeners, estimate, estimate, getLogger, getNofifyListeners, notifyListeners, notifyListeners, removeListener, setNofityListeners

Methods inherited from class java.lang.Object

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

Methods inherited from interface netkit.classifiers.Classifier

addListener, classify, classify, clearListeners, estimate, estimate, getLogger, getNofifyListeners, notifyListeners, notifyListeners, removeListener, setNofityListeners

Constructor Detail

NetworkOnlyBayes

public NetworkOnlyBayes()

Method Detail

getShortName

public java.lang.String getShortName()

Returns:

"NetworkOnlyBayes"

getName

public java.lang.String getName()

Returns:

"Network-Only Bayes NetworkClassifier with MRF internals (NetworkOnlyBayes)"

getDescription

public java.lang.String getDescription()

Returns:

"See reference: [Macskassy, Provost] 'Simple Models' - nBC"

getDefaultConfiguration

public Configuration getDefaultConfiguration()

Create a default configuration for this classifier. It sets the 'sampleneighbors' property to -1. It otherwise uses the configuration from the super class.

Specified by:

getDefaultConfiguration in interface Configurable

Overrides:

getDefaultConfiguration in class NetworkClassifierImp

Returns:

a default configuration with 'sampleneighbors' set to -1.

See Also:

NetworkClassifierImp.getDefaultConfiguration()

configure

public void configure(Configuration config)

Configures this classifier. does the super class configuration and then uses 'sampleneighbors' sets the maximum number of neighbor edges to use. Random sampling will be used if a node has more than this number of edges.

Specified by:

configure in interface Configurable

Overrides:

configure in class NetworkClassifierImp

Parameters:

config - The configuration object to use.

See Also:

NetworkClassifierImp.configure(netkit.util.Configuration)

reset

public void reset()

Resets internal variables.

Specified by:

reset in interface Classifier

Overrides:

reset in class ClassifierImp

See Also:

ClassifierImp.reset()

induceModel

public void induceModel(Graph graph,
                        DataSplit split)

Induce the model by computing the counts for Prob(classIdx | neighborClassIdx)

Specified by:

induceModel in interface Classifier

Overrides:

induceModel in class NetworkClassifierImp

Parameters:

graph - The graph to induce the model over

split - The datasplit telling the classifier which nodes have known labels and which do not

See Also:

NetworkClassifierImp.induceModel(netkit.graph.Graph, netkit.classifiers.DataSplit)

doEstimate

public boolean doEstimate(Node node,
                          double[] estimation)

compute class estimates.

Specified by:

doEstimate in class NetworkClassifierImp

Parameters:

node - The node to estimate class probabilities for

estimation - the double array containing the probability estimates that the node belongs to each of the possible class labels.

Returns:

true

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object