MINND

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.mi.MINND

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.MultiInstanceCapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler
```
public class MINND
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.MultiInstanceCapabilitiesHandler, weka.core.TechnicalInformationHandler
```
Multiple-Instance Nearest Neighbour with Distribution learner.

It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0. In order to avoid overfitting, it uses mean-square function (i.e. the Euclidean distance) to search for the weights.
It then uses the weights to cleanse the training data. After that it searches for the weights again from the starting points of the weights searched before.
Finally it uses the most updated weights to cleanse the test exemplar and then finds the nearest neighbour of the test exemplar using partly-weighted Kullback distance. But the variances in the Kullback distance are the ones before cleansing.

For more information see:

Xin Xu (2001). A nearest distribution approach to multiple-instance learning. Hamilton, NZ.
BibTeX:
```
 @misc{Xu2001,
    address = {Hamilton, NZ},
    author = {Xin Xu},
    note = {0657.591B},
    school = {University of Waikato},
    title = {A nearest distribution approach to multiple-instance learning},
    year = {2001}
 }
 
```
Valid options are:
```
 -K <number of neighbours>
  Set number of nearest neighbour for prediction
  (default 1)
 
```
```
 -S <number of neighbours>
  Set number of nearest neighbour for cleansing the training data
  (default 1)
 
```
```
 -E <number of neighbours>
  Set number of nearest neighbour for cleansing the testing data
  (default 1)
 
```
Version:

$Revision: 10369 $

Author:

Xin Xu (xx5@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

MINND()

Constructors
Constructor and Description
`MINND()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances exs)` As normal Nearest Neighbour algorithm does, it's lazy and simply records the exemplar information (i.e.
`double`	`classifyInstance(weka.core.Instance ex)` Use Kullback Leibler distance to find the nearest neighbours of the given exemplar.
`weka.core.Instance`	`cleanse(weka.core.Instance before)` Cleanse the given exemplar according to the valid and noise data statistics
`void`	`findWeights(int row, double[][] mean)` Use gradient descent to distort the MU parameter for the exemplar.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`weka.core.Capabilities`	`getMultiInstanceCapabilities()` Returns the capabilities of this multi-instance classifier for the relational data.
`int`	`getNumNeighbours()` Returns the number of nearest neighbours to estimate the class prediction of tests bags
`int`	`getNumTestingNoises()` Returns The number of nearest neighbour instances in the selection of noises in the test data
`int`	`getNumTrainingNoises()` Returns the number of nearest neighbour instances in the selection of noises in the training data
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`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.
`java.lang.String`	`globalInfo()` Returns a string describing this filter
`double`	`kullback(double[] mu1, double[] mu2, double[] var1, double[] var2, int pos)` This function calculates the Kullback Leibler distance between two normal distributions.
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options
`static void`	`main(java.lang.String[] args)` Main method for testing.
`java.lang.String`	`numNeighboursTipText()` Returns the tip text for this property
`java.lang.String`	`numTestingNoisesTipText()` Returns the tip text for this property
`java.lang.String`	`numTrainingNoisesTipText()` Returns the tip text for this property
`weka.core.Instance`	`preprocess(weka.core.Instances data, int pos)` Pre-process the given exemplar according to the other exemplars in the given exemplars.
`void`	`setNumNeighbours(int numNeighbour)` Sets the number of nearest neighbours to estimate the class prediction of tests bags
`void`	`setNumTestingNoises(int numTesting)` Sets The number of nearest neighbour exemplars in the selection of noises in the test data
`void`	`setNumTrainingNoises(int numTraining)` Sets the number of nearest neighbour instances in the selection of noises in the training data
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`double`	`target(double[] x, double[][] X, int rowpos, double[] Y)` Compute the target function to minimize in gradient descent The formula is: 1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2 where p is the number of exemplars and Y is the class label.

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

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

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - MINND
```
public MINND()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this filter
    
    Returns:
    
    a description of the filter 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 default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - 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 exs)
                     throws java.lang.Exception
```
    As normal Nearest Neighbour algorithm does, it's lazy and simply records the exemplar information (i.e. mean and variance for each dimension of each exemplar and their classes) when building the model. There is actually no need to store the exemplars themselves.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    exs - the training exemplars
    
    Throws:
    
    java.lang.Exception - if the model cannot be built properly
  - preprocess
```
public weka.core.Instance preprocess(weka.core.Instances data,
                                     int pos)
                              throws java.lang.Exception
```
    Pre-process the given exemplar according to the other exemplars in the given exemplars. It also updates noise data statistics.
    
    Parameters:
    
    data - the whole exemplars
    
    pos - the position of given exemplar in data
    
    Returns:
    
    the processed exemplar
    
    Throws:
    
    java.lang.Exception - if the returned exemplar is wrong
  - findWeights
```
public void findWeights(int row,
                        double[][] mean)
```
    Use gradient descent to distort the MU parameter for the exemplar. The exemplar can be in the specified row in the given matrix, which has numExemplar rows and numDimension columns; or not in the matrix.
    
    Parameters:
    
    row - the given row index
    
    mean -
  - target
```
public double target(double[] x,
                     double[][] X,
                     int rowpos,
                     double[] Y)
```
    Compute the target function to minimize in gradient descent The formula is:
    1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2
    where p is the number of exemplars and Y is the class label. In the case of X=MU, f() is the Euclidean distance between two exemplars together with the related weights and var() is sqrt(numDimension)*(Y-Yi) where Y-Yi is either 0 (when Y==Yi) or 1 (Y!=Yi)
    
    Parameters:
    
    x - the weights of the exemplar in question
    
    rowpos - row index of x in X
    
    Y - the observed class label
    
    Returns:
    
    the result of the target function
  - classifyInstance
```
public double classifyInstance(weka.core.Instance ex)
                        throws java.lang.Exception
```
    Use Kullback Leibler distance to find the nearest neighbours of the given exemplar. It also uses K-Nearest Neighbour algorithm to classify the test exemplar
    
    Specified by:
    
    classifyInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    classifyInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    ex - the given test exemplar
    
    Returns:
    
    the classification
    
    Throws:
    
    java.lang.Exception - if the exemplar could not be classified successfully
  - cleanse
```
public weka.core.Instance cleanse(weka.core.Instance before)
                           throws java.lang.Exception
```
    Cleanse the given exemplar according to the valid and noise data statistics
    
    Parameters:
    
    before - the given exemplar
    
    Returns:
    
    the processed exemplar
    
    Throws:
    
    java.lang.Exception - if the returned exemplar is wrong
  - kullback
```
public double kullback(double[] mu1,
                       double[] mu2,
                       double[] var1,
                       double[] var2,
                       int pos)
```
    This function calculates the Kullback Leibler distance between two normal distributions. This distance is always positive. Kullback Leibler distance = integral{f(X)ln(f(X)/g(X))} Note that X is a vector. Since we assume dimensions are independent f(X)(g(X) the same) is actually the product of normal density functions of each dimensions. Also note that it should be log2 instead of (ln) in the formula, but we use (ln) simply for computational convenience. The result is as follows, suppose there are P dimensions, and f(X) is the first distribution and g(X) is the second: Kullback = sum[1..P](ln(SIGMA2/SIGMA1)) + sum[1..P](SIGMA1^2 / (2*(SIGMA2^2))) + sum[1..P]((MU1-MU2)^2 / (2*(SIGMA2^2))) - P/2
    
    Parameters:
    
    mu1 - mu of the first normal distribution
    
    mu2 - mu of the second normal distribution
    
    var1 - variance(SIGMA^2) of the first normal distribution
    
    var2 - variance(SIGMA^2) of the second normal distribution
    
    Returns:
    
    the Kullback distance of two distributions
  - listOptions
```
public java.util.Enumeration<weka.core.Option> listOptions()
```
    Returns an enumeration describing the available options
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -K <number of neighbours>
  Set number of nearest neighbour for prediction
  (default 1)
 
```
```
 -S <number of neighbours>
  Set number of nearest neighbour for cleansing the training data
  (default 1)
 
```
```
 -E <number of neighbours>
  Set number of nearest neighbour for cleansing the testing data
  (default 1)
 
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - 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.AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - numNeighboursTipText
```
public java.lang.String numNeighboursTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumNeighbours
```
public void setNumNeighbours(int numNeighbour)
```
    Sets the number of nearest neighbours to estimate the class prediction of tests bags
    
    Parameters:
    
    numNeighbour - the number of citers
  - getNumNeighbours
```
public int getNumNeighbours()
```
    Returns the number of nearest neighbours to estimate the class prediction of tests bags
    
    Returns:
    
    the number of neighbours
  - numTrainingNoisesTipText
```
public java.lang.String numTrainingNoisesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumTrainingNoises
```
public void setNumTrainingNoises(int numTraining)
```
    Sets the number of nearest neighbour instances in the selection of noises in the training data
    
    Parameters:
    
    numTraining - the number of noises in training data
  - getNumTrainingNoises
```
public int getNumTrainingNoises()
```
    Returns the number of nearest neighbour instances in the selection of noises in the training data
    
    Returns:
    
    the number of noises in training data
  - numTestingNoisesTipText
```
public java.lang.String numTestingNoisesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumTestingNoises
```
public int getNumTestingNoises()
```
    Returns The number of nearest neighbour instances in the selection of noises in the test data
    
    Returns:
    
    the number of noises in test data
  - setNumTestingNoises
```
public void setNumTestingNoises(int numTesting)
```
    Sets The number of nearest neighbour exemplars in the selection of noises in the test data
    
    Parameters:
    
    numTesting - the number of noises in test data
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method for testing.
    
    Parameters:
    
    args - the options for the classifier

Class MINND

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

MINND

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

getMultiInstanceCapabilities

buildClassifier

preprocess

findWeights

target

classifyInstance

cleanse

kullback

listOptions

setOptions

getOptions

numNeighboursTipText

setNumNeighbours

getNumNeighbours

numTrainingNoisesTipText

setNumTrainingNoises

getNumTrainingNoises

numTestingNoisesTipText

getNumTestingNoises

setNumTestingNoises

getRevision

main