| weka.classifiers.RandomizableClassifier
 weka.classifiers.mi.MIEMDD
| MIEMDD | | public class MIEMDD extends RandomizableClassifier implements OptionHandler,MultiInstanceCapabilitiesHandler,TechnicalInformationHandler(Code) | |
EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM. In this implementation, we use most-likely cause DD model and only use 3 random selected postive bags as initial starting points of EM.
For more information see:
Qi Zhang, Sally A. Goldman: EM-DD: An Improved Multiple-Instance Learning Technique. In: Advances in Neural Information Processing Systems 14, 1073-108, 2001.
BibTeX:
@inproceedings{Zhang2001,
author = {Qi Zhang and Sally A. Goldman},
booktitle = {Advances in Neural Information Processing Systems 14},
pages = {1073-108},
publisher = {MIT Press},
title = {EM-DD: An Improved Multiple-Instance Learning Technique},
year = {2001}
}
Valid options are:
-N <num>
Whether to 0=normalize/1=standardize/2=neither.
(default 1=standardize)
-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:
Eibe Frank (eibe@cs.waikato.ac.nz)
author:
Lin Dong (ld21@cs.waikato.ac.nz)
version:
$Revision: 1.5 $ |
| FILTER_NONE | | final public static int FILTER_NONE(Code) | | No normalization/standardization
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| FILTER_NORMALIZE | | final public static int FILTER_NORMALIZE(Code) | | Normalize training data
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| FILTER_STANDARDIZE | | final public static int FILTER_STANDARDIZE(Code) | | Standardize training data
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| TAGS_FILTER | | final public static Tag[] TAGS_FILTER(Code) | | The filter to apply to the training data
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| m_ClassIndex | | protected int m_ClassIndex(Code) | | The index of the class attribute
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| m_Classes | | protected int[] m_Classes(Code) | | Class labels for each bag
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| m_Data | | protected double[][][] m_Data(Code) | | MI data
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| m_Filter | | protected Filter m_Filter(Code) | | The filter used to standardize/normalize all values.
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| m_NumClasses | | protected int m_NumClasses(Code) | | The number of the class labels
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| m_Par | | protected double[] m_Par(Code) | | |
| m_emData | | protected double[][] m_emData(Code) | | MI data
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| m_filterType | | protected int m_filterType(Code) | | Whether to normalize/standardize/neither, default:standardize
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| serialVersionUID | | final static long serialVersionUID(Code) | | for serialization
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| buildClassifier | | public void buildClassifier(Instances train) throws Exception(Code) | | Builds the classifier
Parameters:
train - the training data to be used for generating theboosted classifier.
throws:
Exception - if the classifier could not be built successfully |
| distributionForInstance | | public double[] distributionForInstance(Instance exmp) throws Exception(Code) | | Computes the distribution for a given exemplar
Parameters:
exmp - the exemplar for which distribution is computed the distribution
throws:
Exception - if the distribution can't be computed successfully |
| filterTypeTipText | | public String filterTypeTipText()(Code) | | Returns the tip text for this property
tip text for this property suitable fordisplaying in the explorer/experimenter gui |
| findInstance | | protected int findInstance(int i, double[] x)(Code) | | given x, find the instance in ith bag with the most likelihood
probability, which is most likely to responsible for the label of the
bag For a positive bag, find the instance with the maximal probability
of being positive For a negative bag, find the instance with the minimal
probability of being negative
Parameters:
i - the bag index
Parameters:
x - the current values of variables index of the instance in the bag |
| getCapabilities | | public Capabilities getCapabilities()(Code) | | Returns default capabilities of the classifier.
the capabilities of this classifier |
| getFilterType | | public SelectedTag getFilterType()(Code) | | Gets how the training data will be transformed. Will be one of
FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
the filtering mode |
| getMultiInstanceCapabilities | | public Capabilities getMultiInstanceCapabilities()(Code) | | Returns the capabilities of this multi-instance classifier for the
relational data.
the capabilities of this object
See Also: Capabilities |
| getOptions | | public String[] getOptions()(Code) | | Gets the current settings of the classifier.
an array of strings suitable for passing to setOptions |
| getTechnicalInformation | | public TechnicalInformation getTechnicalInformation()(Code) | | 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.
the technical information about this class |
| globalInfo | | public String globalInfo()(Code) | | Returns a string describing this filter
a description of the filter suitable fordisplaying in the explorer/experimenter gui |
| listOptions | | public Enumeration listOptions()(Code) | | Returns an enumeration describing the available options
an enumeration of all the available options |
| main | | public static void main(String[] argv)(Code) | | Main method for testing this class.
Parameters:
argv - should contain the command line arguments to thescheme (see Evaluation) |
| setFilterType | | public void setFilterType(SelectedTag newType)(Code) | | Sets how the training data will be transformed. Should be one of
FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
Parameters:
newType - the new filtering mode |
| setOptions | | public void setOptions(String[] options) throws Exception(Code) | | Parses a given list of options.
Valid options are:
-N <num>
Whether to 0=normalize/1=standardize/2=neither.
(default 1=standardize)
-S <num>
Random number seed.
(default 1)
-D
If set, classifier is run in debug mode and
may output additional info to the console
Parameters:
options - the list of options as an array of strings
throws:
Exception - if an option is not supported |
| toString | | public String toString()(Code) | | Gets a string describing the classifier.
a string describing the classifer built. |
| Fields inherited from weka.classifiers.RandomizableClassifier | protected int m_Seed(Code)(Java Doc)
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