jsat.classifiers.bayesian

Class AODE

java.lang.Object

jsat.classifiers.BaseUpdateableClassifier

jsat.classifiers.bayesian.AODE

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableClassifier

public class AODE
extends BaseUpdateableClassifier

Averaged One-Dependence Estimators (AODE) is an extension of Naive Bayes that attempts to be more accurate by reducing the independence assumption. For n data points with d categorical features, d ODE classifier are created, each with a dependence on a different attribute. The results of these classifiers is averaged to produce a final result. The construction time is O(n d²). Because of this extra dependence requirement, the implementation only allows for categorical features.

See: Webb, G., & Boughton, J. (2005). Not so naive bayes: Aggregating one-dependence estimators. Machine Learning, 1–24. Retrieved from here

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
AODE() Creates a new AODE classifier.

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
AODE	clone()
double	getM() Returns the minimum needed score
void	setM(double m) Sets the minimum prior observation value needed for an attribute combination to have enough support to be included in the final estimate.
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting) Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.
void	update(DataPoint dataPoint, int targetClass) Updates the classifier by giving it a new data point to learn from.

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

getEpochs, setEpochs, train, trainEpochs

Methods inherited from class java.lang.Object

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

Constructor Detail

AODE

public AODE()

Creates a new AODE classifier.

Method Detail

clone

public AODE clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface UpdateableClassifier

Specified by:

clone in class BaseUpdateableClassifier

setUp

public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)

Description copied from interface: UpdateableClassifier

Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.

Parameters:

categoricalAttributes - an array containing the categorical attributes that will be in each data point

numericAttributes - the number of numeric attributes that will be in each data point

predicting - the information for the target class that will be predicted

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Specified by:

train in interface Classifier

Overrides:

train in class BaseUpdateableClassifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

update

public void update(DataPoint dataPoint,
                   int targetClass)

Description copied from interface: UpdateableClassifier

Updates the classifier by giving it a new data point to learn from.

Parameters:

dataPoint - the data point to learn

targetClass - the target class of the data point

classify

public CategoricalResults classify(DataPoint data)

Description copied from interface: Classifier

Performs classification on the given data point.

Parameters:

data - the data point to classify

Returns:

the results of the classification.

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Classifier

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Returns:

true if the model supports weighted data, false otherwise

setM

public void setM(double m)

Sets the minimum prior observation value needed for an attribute combination to have enough support to be included in the final estimate.

Parameters:

m - the minimum needed score

getM

public double getM()

Returns the minimum needed score

Returns:

the minimum needed score