Package weka.classifiers.mi

Class MIOptimalBall

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
    public class MIOptimalBall
extends Classifier
implements OptionHandler, WeightedInstancesHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler
    This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center. The possible ball center is a certain instance in a positive bag. The possible radiuses are those which can achieve the highest classification accuracy. The model selects the maximum radius as the radius of the optimal ball.

    For more information about this algorithm, see:

    Peter Auer, Ronald Ortner: A Boosting Approach to Multiple Instance Learning. In: 15th European Conference on Machine Learning, 63-74, 2004.

    BibTeX: 

     @inproceedings{Auer2004,
    author = {Peter Auer and Ronald Ortner},
    booktitle = {15th European Conference on Machine Learning},
    note = {LNAI 3201},
    pages = {63-74},
    publisher = {Springer},
    title = {A Boosting Approach to Multiple Instance Learning},
    year = {2004}
 }

    Valid options are: 

     -N <num>
  Whether to 0=normalize/1=standardize/2=neither. 
  (default 0=normalize)
    Version:
    $Revision: 9144 $
    Author:
    Lin Dong (ld21@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • Field Detail

      • FILTER_NORMALIZE

        public static final int FILTER_NORMALIZE
        Normalize training data
        See Also:
        Constant Field Values

      • FILTER_STANDARDIZE

        public static final int FILTER_STANDARDIZE
        Standardize training data
        See Also:
        Constant Field Values

      • FILTER_NONE

        public static final int FILTER_NONE
        No normalization/standardization
        See Also:
        Constant Field Values

      • TAGS_FILTER

        public static final Tag[] TAGS_FILTER
        The filter to apply to the training data

    • Constructor Detail

      • MIOptimalBall

        public MIOptimalBall()

    • 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 data)
                     throws java.lang.Exception
        Builds the classifier
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        data - the training data to be used for generating the boosted classifier.
        Throws:
        java.lang.Exception - if the classifier could not be built successfully

      • calculateDistance

        public void calculateDistance​(Instances train)
        calculate the distances from each instance in a positive bag to each bag. All result distances are stored in m_Distance[i][j][k], where m_Distance[i][j][k] refers the distances from the jth instance in ith bag to the kth bag
        Parameters:
        train - the multi-instance dataset (with relational attribute)

      • minBagDistance

        public double minBagDistance​(Instance center,
                             Instance bag)
        Calculate the distance from one data point to a bag
        Parameters:
        center - the data point in instance space
        bag - the bag
        Returns:
        the double value as the distance.

      • findRadius

        public void findRadius​(Instances train)
        Find the maximum radius for the optimal ball.
        Parameters:
        train - the multi-instance data

      • sortArray

        public double[] sortArray​(double[] distance)
        Sort the array.
        Parameters:
        distance - the array need to be sorted
        Returns:
        sorted array

      • distributionForInstance

        public double[] distributionForInstance​(Instance newBag)
                                 throws java.lang.Exception
        Computes the distribution for a given multiple instance
        Overrides:
        distributionForInstance in class Classifier
        Parameters:
        newBag - the instance for which distribution is computed
        Returns:
        the distribution
        Throws:
        java.lang.Exception - if the distribution can't be computed successfully

      • 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.

      • 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

      • setOptions

        public void setOptions​(java.lang.String[] options)
                throws java.lang.Exception
        Parses a given list of options.

        Valid options are: 

         -N <num>
  Whether to 0=normalize/1=standardize/2=neither. 
  (default 0=normalize)
        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

      • filterTypeTipText

        public java.lang.String filterTypeTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setFilterType

        public void setFilterType​(SelectedTag newType)
        Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
        Parameters:
        newType - the new filtering mode

      • getFilterType

        public SelectedTag getFilterType()
        Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
        Returns:
        the filtering mode

      • main

        public static void main​(java.lang.String[] argv)
        Main method for testing this class.
        Parameters:
        argv - should contain the command line arguments to the scheme (see Evaluation)