|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD | |||||||||
java.lang.Objectweka.classifiers.Classifier
weka.classifiers.RandomizableClassifier
weka.classifiers.mi.MIEMDD
public class MIEMDD
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.
@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
| Field Summary | |
|---|---|
static int |
FILTER_NONE
No normalization/standardization |
static int |
FILTER_NORMALIZE
Normalize training data |
static int |
FILTER_STANDARDIZE
Standardize training data |
static Tag[] |
TAGS_FILTER
The filter to apply to the training data |
| Constructor Summary | |
|---|---|
MIEMDD()
|
|
| Method Summary | |
|---|---|
void |
buildClassifier(Instances train)
Builds the classifier |
double[] |
distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
java.lang.String |
filterTypeTipText()
Returns the tip text for this property |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
SelectedTag |
getFilterType()
Gets how the training data will be transformed. |
Capabilities |
getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational 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 |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
void |
setFilterType(SelectedTag newType)
Sets how the training data will be transformed. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
java.lang.String |
toString()
Gets a string describing the classifier. |
| Methods inherited from class weka.classifiers.RandomizableClassifier |
|---|
getSeed, seedTipText, setSeed |
| Methods inherited from class weka.classifiers.Classifier |
|---|
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
|---|
public static final int FILTER_NORMALIZE
public static final int FILTER_STANDARDIZE
public static final int FILTER_NONE
public static final Tag[] TAGS_FILTER
| Constructor Detail |
|---|
public MIEMDD()
| Method Detail |
|---|
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class RandomizableClassifier
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-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
setOptions in interface OptionHandlersetOptions in class RandomizableClassifieroptions - the list of options as an array of strings
java.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class RandomizableClassifierpublic java.lang.String filterTypeTipText()
public SelectedTag getFilterType()
public void setFilterType(SelectedTag newType)
newType - the new filtering modepublic Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class ClassifierCapabilitiespublic Capabilities getMultiInstanceCapabilities()
getMultiInstanceCapabilities in interface MultiInstanceCapabilitiesHandlerCapabilities
public void buildClassifier(Instances train)
throws java.lang.Exception
buildClassifier in class Classifiertrain - the training data to be used for generating the
boosted classifier.
java.lang.Exception - if the classifier could not be built successfully
public double[] distributionForInstance(Instance exmp)
throws java.lang.Exception
distributionForInstance in class Classifierexmp - the exemplar for which distribution is computed
java.lang.Exception - if the distribution can't be computed successfullypublic java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Classifierpublic static void main(java.lang.String[] argv)
argv - should contain the command line arguments to the
scheme (see Evaluation)
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD | |||||||||