Package moa.classifiers.lazy

Class SAMkNN

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.lazy.SAMkNN

All Implemented Interfaces:

Configurable, java.io.Serializable, CapabilitiesHandler, Classifier, MultiClassClassifier, AWTRenderable, Learner<Example<Instance>>, MOAObject, OptionHandler

public class SAMkNN
extends AbstractClassifier
implements MultiClassClassifier, CapabilitiesHandler

Self Adjusting Memory (SAM) coupled with the k Nearest Neighbor classifier (kNN) .

Valid options are:

-k number of neighbours
-w max instances
-m minimum number of instances in the STM
-p LTM size relative to max instances
-r Recalculation of the STM error

Author:

Viktor Losing ([email protected]) Paper: "KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift" Viktor Losing, Barbara Hammer and Heiko Wersing http://ieeexplore.ieee.org/document/7837853 PDF can be found at https://pub.uni-bielefeld.de/download/2907622/2907623 BibTex: "@INPROCEEDINGS{7837853, author={V. Losing and B. Hammer and H. Wersing}, booktitle={2016 IEEE 16th International Conference on Data Mining (ICDM)}, title={KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift}, year={2016}, pages={291-300}, keywords={data mining;optimisation;pattern classification;Big Data;Internet of Things;KNN classifier;SAM-kNN robustness;data mining;k nearest neighbor algorithm;metaparameter optimization;nonstationary data streams;performance evaluation;self adjusting memory model;Adaptation models;Benchmark testing;Biological system modeling;Data mining;Heuristic algorithms;Prediction algorithms;Predictive models;Data streams;concept drift;data mining;kNN}, doi={10.1109/ICDM.2016.0040}, month={Dec} }"

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
IntOption	kOption
IntOption	limitOption
IntOption	minSTMSizeOption
FlagOption	recalculateSTMErrorOption
FloatOption	relativeLTMSizeOption

Fields inherited from class moa.classifiers.AbstractClassifier

classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

config

Constructor Summary

Constructors

Constructor	Description
SAMkNN()

Method Summary

Modifier and Type	Method	Description
ImmutableCapabilities	defineImmutableCapabilities()	Defines the set of capabilities the object has.
void	getModelDescription(java.lang.StringBuilder out, int indent)	Returns a string representation of the model.
protected Measurement[]	getModelMeasurementsImpl()	Gets the current measurements of this classifier. The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.
java.lang.String	getPurposeString()	Dictionary with option texts and objects
double[]	getVotesForInstance(Instance inst)	Predicts the label of a given sample by using the STM, LTM and the CM.
protected void	init()
boolean	isRandomizable()	Gets whether this learner needs a random seed.
void	resetLearningImpl()	Resets this classifier.
void	setModelContext(InstancesHeader context)	Sets the reference to the header of the data stream.
void	trainOnInstanceImpl(Instance inst)	Trains this classifier incrementally using the given instance. The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods.

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

Methods inherited from class moa.options.AbstractOptionHandler

getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from interface moa.capabilities.CapabilitiesHandler

getCapabilities

Methods inherited from interface moa.MOAObject

measureByteSize

Methods inherited from class java.lang.Object

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

Methods inherited from interface moa.options.OptionHandler

getCLICreationString, getOptions, prepareForUse, prepareForUse

Field Detail

kOption

public IntOption kOption

limitOption

public IntOption limitOption

minSTMSizeOption

public IntOption minSTMSizeOption

relativeLTMSizeOption

public FloatOption relativeLTMSizeOption

recalculateSTMErrorOption

public FlagOption recalculateSTMErrorOption

Constructor Detail

SAMkNN

public SAMkNN()

Method Detail

getPurposeString

public java.lang.String getPurposeString()

Description copied from class: AbstractOptionHandler

Dictionary with option texts and objects

Specified by:

getPurposeString in interface OptionHandler

Overrides:

getPurposeString in class AbstractClassifier

Returns:

the string with the purpose of this object

init

protected void init()

setModelContext

public void setModelContext(InstancesHeader context)

Description copied from interface: Learner

Sets the reference to the header of the data stream. The header of the data stream is extended from WEKA Instances. This header is needed to know the number of classes and attributes

Specified by:

setModelContext in interface Learner<Example<Instance>>

Overrides:

setModelContext in class AbstractClassifier

Parameters:

context - the reference to the data stream header

resetLearningImpl

public void resetLearningImpl()

Description copied from class: AbstractClassifier

Resets this classifier. It must be similar to starting a new classifier from scratch.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

resetLearningImpl in class AbstractClassifier

trainOnInstanceImpl

public void trainOnInstanceImpl(Instance inst)

Description copied from class: AbstractClassifier

Trains this classifier incrementally using the given instance.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

trainOnInstanceImpl in class AbstractClassifier

Parameters:

inst - the instance to be used for training

getVotesForInstance

public double[] getVotesForInstance(Instance inst)

Predicts the label of a given sample by using the STM, LTM and the CM.

Specified by:

getVotesForInstance in interface Classifier

Specified by:

getVotesForInstance in class AbstractClassifier

Parameters:

inst - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class

getModelMeasurementsImpl

protected Measurement[] getModelMeasurementsImpl()

Description copied from class: AbstractClassifier

Gets the current measurements of this classifier.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Specified by:

getModelMeasurementsImpl in class AbstractClassifier

Returns:

an array of measurements to be used in evaluation tasks

getModelDescription

public void getModelDescription(java.lang.StringBuilder out,
                                int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Specified by:

getModelDescription in class AbstractClassifier

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

isRandomizable

public boolean isRandomizable()

Description copied from interface: Learner

Gets whether this learner needs a random seed. Examples of methods that needs a random seed are bagging and boosting.

Specified by:

isRandomizable in interface Learner<Example<Instance>>

Returns:

true if the learner needs a random seed.

defineImmutableCapabilities

public ImmutableCapabilities defineImmutableCapabilities()

Description copied from interface: CapabilitiesHandler

Defines the set of capabilities the object has. Should be overridden if the object's capabilities do not change.

Specified by:

defineImmutableCapabilities in interface CapabilitiesHandler

Overrides:

defineImmutableCapabilities in class AbstractClassifier

Returns:

The capabilities of the object.