MITI

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.RandomizableClassifier
  - - weka.classifiers.mi.MITI

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.MultiInstanceCapabilitiesHandler, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

Direct Known Subclasses:: MIRI

public class MITI
extends weka.classifiers.RandomizableClassifier
implements weka.core.OptionHandler, weka.core.AdditionalMeasureProducer, weka.core.TechnicalInformationHandler, weka.core.MultiInstanceCapabilitiesHandler

MITI (Multi Instance Tree Inducer): multi-instance classification based a decision tree learned using Blockeel et al.'s algorithm. For more information, see

Hendrik Blockeel, David Page, Ashwin Srinivasan: Multi-instance Tree Learning. In: Proceedings of the International Conference on Machine Learning, 57-64, 2005.

Luke Bjerring, Eibe Frank: Beyond Trees: Adopting MITI to Learn Rules and Ensemble Classifiers for Multi-instance Data. In: Proceedings of the Australasian Joint Conference on Artificial Intelligence, 2011.

BibTeX:

 @inproceedings{Blockeel2005,
    author = {Hendrik Blockeel and David Page and Ashwin Srinivasan},
    booktitle = {Proceedings of the International Conference on Machine Learning},
    pages = {57-64},
    publisher = {ACM},
    title = {Multi-instance Tree Learning},
    year = {2005}
 }
 
 @inproceedings{Bjerring2011,
    author = {Luke Bjerring and Eibe Frank},
    booktitle = {Proceedings of the Australasian Joint Conference on Artificial Intelligence},
    publisher = {Springer},
    title = {Beyond Trees: Adopting MITI to Learn Rules and Ensemble Classifiers for Multi-instance Data},
    year = {2011}
 }

Valid options are:

 -M [1|2|3]
  The method used to determine best split:
  1. Gini; 2. MaxBEPP; 3. SSBEPP

 -K [kBEPPConstant]
  The constant used in the tozero() hueristic

 -L
  Scales the value of K to the size of the bags

 -U
  Use unbiased estimate rather than BEPP, i.e. UEPP.

 -B
  Uses the instances present for the bag counts at each node when splitting,
  weighted according to 1 - Ba ^ n, where n is the number of instances
  present which belong to the bag, and Ba is another parameter (default 0.5)

 -Ba [multiplier]
  Multiplier for count influence of a bag based on the number of its instances

 -A [number of attributes]
  The number of randomly selected attributes to split
  -1: All attributes
  -2: square root of the total number of attributes

 -An [number of splits]
  The number of top scoring attribute splits to randomly pick from
  -1: All splits (completely random selection)
  -2: square root of the number of splits

 -S <num>
  Random number seed.
  (default 1)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Author:: Luke Bjerring, Eibe Frank
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

static int SPLITMETHOD_GINI

static int SPLITMETHOD_MAXBEPP

static int SPLITMETHOD_SSBEPP

static weka.core.Tag[] TAGS_SPLITMETHOD
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Fields
Modifier and Type	Field and Description
`static int`	`SPLITMETHOD_GINI`
`static int`	`SPLITMETHOD_MAXBEPP`
`static int`	`SPLITMETHOD_SSBEPP`
`static weka.core.Tag[]`	`TAGS_SPLITMETHOD`

Constructor Summary

Constructors
Constructor and Description

MITI()

Constructors
Constructor and Description
`MITI()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`attributesToSplitTipText()` Help for attributes to split
`java.lang.String`	`baTipText()` Help for bag-based stats parameter.
`java.lang.String`	`bTipText()` Help for bag-based stats flag.
`void`	`buildClassifier(weka.core.Instances trainingData)` Learns the classifier from the training data.
`double[]`	`distributionForInstance(weka.core.Instance newBag)` Returns the "class distribution" for the given bag.
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`int`	`getAttributesToSplit()` Getter method.
`boolean`	`getB()` Getter for B.
`double`	`getBa()` Getter for Ba.
`weka.core.Capabilities`	`getCapabilities()` Returns the capabilities of this classifier.
`int`	`getK()` Getter for K.
`boolean`	`getL()` Getter for L.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure.
`weka.core.Capabilities`	`getMultiInstanceCapabilities()` Returns the capabilities of this multi-instance classifier for the relational data.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`weka.core.SelectedTag`	`getSplitMethod()` Getter method.
`weka.core.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.
`int`	`getTopNAttributesToSplit()` Getter method.
`boolean`	`getUnbiasedEstimate()` Getter for unbiased estimate flag.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.lang.String`	`kTipText()` Help for K parameter.
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Lists the options for this classifier.
`java.lang.String`	`lTipText()` Help for scale parameter.
`static void`	`main(java.lang.String[] options)` Used to run the classifier from the command-line.
`void`	`setAttributesToSplit(int newValue)` Setter method.
`void`	`setB(boolean newValue)` Setter for B.
`void`	`setBa(double newValue)` Setter for Ba.
`void`	`setK(int newValue)` Setter for K.
`void`	`setL(boolean newValue)` Setter for L.
`void`	`setOptions(java.lang.String[] options)` Determines the settings of the Classifier.
`void`	`setSplitMethod(weka.core.SelectedTag newMethod)` Setter method.
`void`	`setTopNAttributesToSplit(int newValue)` Setter method.
`void`	`setUnbiasedEstimate(boolean newValue)` Setter for unbiased estimate flag.
`java.lang.String`	`splitMethodTipText()` Help for split measure selection.
`java.lang.String`	`topNAttributesToSplitTipText()` Help for top-N attributes to split
`java.lang.String`	`toString()` Outputs tree as a string.
`java.lang.String`	`unbiasedEstimateTipText()` Help for unbiased estimate flag.

Methods inherited from class weka.classifiers.RandomizableClassifier
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Field Detail
  - SPLITMETHOD_GINI
```
public static final int SPLITMETHOD_GINI
```
    See Also:
    
    Constant Field Values
  - SPLITMETHOD_MAXBEPP
```
public static final int SPLITMETHOD_MAXBEPP
```
    See Also:
    
    Constant Field Values
  - SPLITMETHOD_SSBEPP
```
public static final int SPLITMETHOD_SSBEPP
```
    See Also:
    
    Constant Field Values
  - TAGS_SPLITMETHOD
```
public static final weka.core.Tag[] TAGS_SPLITMETHOD
```
- Constructor Detail
  - MITI
```
public MITI()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public weka.core.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 weka.core.TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns the capabilities of this classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
  - getMultiInstanceCapabilities
```
public weka.core.Capabilities getMultiInstanceCapabilities()
```
    Returns the capabilities of this multi-instance classifier for the relational data.
    
    Specified by:
    
    getMultiInstanceCapabilities in interface weka.core.MultiInstanceCapabilitiesHandler
    
    Returns:
    
    the capabilities of this object
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(weka.core.Instances trainingData)
                     throws java.lang.Exception
```
    Learns the classifier from the training data.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Throws:
    
    java.lang.Exception
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> enumerateMeasures()
```
    Returns an enumeration of the additional measure names.
    
    Specified by:
    
    enumerateMeasures in interface weka.core.AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure.
    
    Specified by:
    
    getMeasure in interface weka.core.AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance newBag)
                                 throws java.lang.Exception
```
    Returns the "class distribution" for the given bag.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Throws:
    
    java.lang.Exception
  - listOptions
```
public java.util.Enumeration<weka.core.Option> listOptions()
```
    Lists the options for this classifier. Valid options are: Valid options are:
```
 -M [1|2|3]
  The method used to determine best split:
  1. Gini; 2. MaxBEPP; 3. SSBEPP
 
```
```
 -K [kBEPPConstant]
  The constant used in the tozero() hueristic
 
```
```
 -L
  Scales the value of K to the size of the bags
 
```
```
 -U
  Use unbiased estimate rather than BEPP, i.e. UEPP.
 
```
```
 -B
  Uses the instances present for the bag counts at each node when splitting,
  weighted according to 1 - Ba ^ n, where n is the number of instances
  present which belong to the bag, and Ba is another parameter (default 0.5)
 
```
```
 -Ba [multiplier]
  Multiplier for count influence of a bag based on the number of its instances
 
```
```
 -A [number of attributes]
  The number of randomly selected attributes to split
  -1: All attributes
  -2: square root of the total number of attributes
 
```
```
 -An [number of splits]
  The number of top scoring attribute splits to randomly pick from
  -1: All splits (completely random selection)
  -2: square root of the number of splits
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.RandomizableClassifier
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Determines the settings of the Classifier.
    
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.RandomizableClassifier
    
    Throws:
    
    java.lang.Exception
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.RandomizableClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - kTipText
```
public java.lang.String kTipText()
```
    Help for K parameter.
  - getK
```
public int getK()
```
    Getter for K.
  - setK
```
public void setK(int newValue)
```
    Setter for K.
  - lTipText
```
public java.lang.String lTipText()
```
    Help for scale parameter.
  - getL
```
public boolean getL()
```
    Getter for L.
  - setL
```
public void setL(boolean newValue)
```
    Setter for L.
  - unbiasedEstimateTipText
```
public java.lang.String unbiasedEstimateTipText()
```
    Help for unbiased estimate flag.
  - getUnbiasedEstimate
```
public boolean getUnbiasedEstimate()
```
    Getter for unbiased estimate flag.
  - setUnbiasedEstimate
```
public void setUnbiasedEstimate(boolean newValue)
```
    Setter for unbiased estimate flag.
  - bTipText
```
public java.lang.String bTipText()
```
    Help for bag-based stats flag.
  - getB
```
public boolean getB()
```
    Getter for B.
  - setB
```
public void setB(boolean newValue)
```
    Setter for B.
  - baTipText
```
public java.lang.String baTipText()
```
    Help for bag-based stats parameter.
  - getBa
```
public double getBa()
```
    Getter for Ba.
  - setBa
```
public void setBa(double newValue)
```
    Setter for Ba.
  - attributesToSplitTipText
```
public java.lang.String attributesToSplitTipText()
```
    Help for attributes to split
  - getAttributesToSplit
```
public int getAttributesToSplit()
```
    Getter method.
  - setAttributesToSplit
```
public void setAttributesToSplit(int newValue)
```
    Setter method.
  - topNAttributesToSplitTipText
```
public java.lang.String topNAttributesToSplitTipText()
```
    Help for top-N attributes to split
  - getTopNAttributesToSplit
```
public int getTopNAttributesToSplit()
```
    Getter method.
  - setTopNAttributesToSplit
```
public void setTopNAttributesToSplit(int newValue)
```
    Setter method.
  - splitMethodTipText
```
public java.lang.String splitMethodTipText()
```
    Help for split measure selection.
  - setSplitMethod
```
public void setSplitMethod(weka.core.SelectedTag newMethod)
```
    Setter method.
  - getSplitMethod
```
public weka.core.SelectedTag getSplitMethod()
```
    Getter method.
  - toString
```
public java.lang.String toString()
```
    Outputs tree as a string.
    
    Overrides:
    
    toString in class java.lang.Object
  - main
```
public static void main(java.lang.String[] options)
```
    Used to run the classifier from the command-line.

Class MITI

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

SPLITMETHOD_GINI

SPLITMETHOD_MAXBEPP

SPLITMETHOD_SSBEPP

TAGS_SPLITMETHOD

Constructor Detail

MITI

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

getMultiInstanceCapabilities

buildClassifier

enumerateMeasures

getMeasure

distributionForInstance

listOptions

setOptions

getOptions

kTipText

getK

setK

lTipText

getL

setL

unbiasedEstimateTipText

getUnbiasedEstimate

setUnbiasedEstimate

bTipText

getB

setB

baTipText

getBa

setBa

attributesToSplitTipText

getAttributesToSplit

setAttributesToSplit

topNAttributesToSplitTipText

getTopNAttributesToSplit

setTopNAttributesToSplit

splitMethodTipText

setSplitMethod

getSplitMethod

toString

main