Class MINND

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

    public class MINND
    extends Classifier
    implements OptionHandler, MultiInstanceCapabilitiesHandler, 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: 9144 $
    Author:
    Xin Xu (xx5@cs.waikato.ac.nz)
    See Also:
    Serialized Form
    • Constructor Summary

      Constructors 
      Constructor Description
      MINND()  
    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      void buildClassifier​(Instances exs)
      As normal Nearest Neighbour algorithm does, it's lazy and simply records the exemplar information (i.e.
      double classifyInstance​(Instance ex)
      Use Kullback Leibler distance to find the nearest neighbours of the given exemplar.
      Instance cleanse​(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.
      Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      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.
      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 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
      Instance preprocess​(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
      • Methods inherited from class java.lang.Object

        equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    • 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 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 TechnicalInformationHandler
        Returns:
        the technical information about this class
      • buildClassifier

        public void buildClassifier​(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 class Classifier
        Parameters:
        exs - the training exemplars
        Throws:
        java.lang.Exception - if the model cannot be built properly
      • preprocess

        public Instance preprocess​(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​(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
        Overrides:
        classifyInstance in class Classifier
        Parameters:
        ex - the given test exemplar
        Returns:
        the classification
        Throws:
        java.lang.Exception - if the exemplar could not be classified successfully
      • cleanse

        public Instance cleanse​(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 listOptions()
        Returns an enumeration describing the available options
        Specified by:
        listOptions in interface OptionHandler
        Overrides:
        listOptions in class Classifier
        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 OptionHandler
        Overrides:
        setOptions in class Classifier
        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 OptionHandler
        Overrides:
        getOptions in class Classifier
        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
      • main

        public static void main​(java.lang.String[] args)
        Main method for testing.
        Parameters:
        args - the options for the classifier