类 MIOptimalBall

java.lang.Object
weka.classifiers.Classifier
weka.classifiers.mi.MIOptimalBall
所有已实现的接口:
Serializable, Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

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)
版本:
$Revision: 9144 $
作者:
Lin Dong (ld21@cs.waikato.ac.nz)
另请参阅:
  • 字段详细资料

    • FILTER_NORMALIZE

      public static final int FILTER_NORMALIZE
      Normalize training data
      另请参阅:
    • FILTER_STANDARDIZE

      public static final int FILTER_STANDARDIZE
      Standardize training data
      另请参阅:
    • FILTER_NONE

      public static final int FILTER_NONE
      No normalization/standardization
      另请参阅:
    • TAGS_FILTER

      public static final Tag[] TAGS_FILTER
      The filter to apply to the training data
  • 构造器详细资料

    • MIOptimalBall

      public MIOptimalBall()
  • 方法详细资料

    • globalInfo

      public String globalInfo()
      Returns a string describing this filter
      返回:
      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.
      指定者:
      getTechnicalInformation 在接口中 TechnicalInformationHandler
      返回:
      the technical information about this class
    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      指定者:
      getCapabilities 在接口中 CapabilitiesHandler
      覆盖:
      getCapabilities 在类中 Classifier
      返回:
      the capabilities of this classifier
      另请参阅:
    • getMultiInstanceCapabilities

      public Capabilities getMultiInstanceCapabilities()
      Returns the capabilities of this multi-instance classifier for the relational data.
      指定者:
      getMultiInstanceCapabilities 在接口中 MultiInstanceCapabilitiesHandler
      返回:
      the capabilities of this object
      另请参阅:
    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Builds the classifier
      指定者:
      buildClassifier 在类中 Classifier
      参数:
      data - the training data to be used for generating the boosted classifier.
      抛出:
      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
      参数:
      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
      参数:
      center - the data point in instance space
      bag - the bag
      返回:
      the double value as the distance.
    • findRadius

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

      public double[] sortArray(double[] distance)
      Sort the array.
      参数:
      distance - the array need to be sorted
      返回:
      sorted array
    • distributionForInstance

      public double[] distributionForInstance(Instance newBag) throws Exception
      Computes the distribution for a given multiple instance
      覆盖:
      distributionForInstance 在类中 Classifier
      参数:
      newBag - the instance for which distribution is computed
      返回:
      the distribution
      抛出:
      Exception - if the distribution can't be computed successfully
    • listOptions

      public Enumeration listOptions()
      Returns an enumeration describing the available options.
      指定者:
      listOptions 在接口中 OptionHandler
      覆盖:
      listOptions 在类中 Classifier
      返回:
      an enumeration of all the available options.
    • getOptions

      public String[] getOptions()
      Gets the current settings of the classifier.
      指定者:
      getOptions 在接口中 OptionHandler
      覆盖:
      getOptions 在类中 Classifier
      返回:
      an array of strings suitable for passing to setOptions
    • setOptions

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

      Valid options are:

       -N <num>
        Whether to 0=normalize/1=standardize/2=neither. 
        (default 0=normalize)
      指定者:
      setOptions 在接口中 OptionHandler
      覆盖:
      setOptions 在类中 Classifier
      参数:
      options - the list of options as an array of strings
      抛出:
      Exception - if an option is not supported
    • filterTypeTipText

      public String filterTypeTipText()
      Returns the tip text for this property
      返回:
      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.
      参数:
      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.
      返回:
      the filtering mode
    • getRevision

      public String getRevision()
      Returns the revision string.
      指定者:
      getRevision 在接口中 RevisionHandler
      覆盖:
      getRevision 在类中 Classifier
      返回:
      the revision
    • main

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