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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* Vote.java
* Copyright (C) 2000 University of Waikato
*
*/
package weka.classifiers.meta;
import weka.classifiers.RandomizableMultipleClassifiersCombiner;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
/**
* Class for combining classifiers. Different combinations of probability estimates for classification are available.
*
* For more information see:
*
* Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc..
*
* J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(3):226-239.
*
*
* Valid options are:
*
* -S <num>
* Random number seed.
* (default 1)
*
* -B <classifier specification>
* Full class name of classifier to include, followed
* by scheme options. May be specified multiple times.
* (default: "weka.classifiers.rules.ZeroR")
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
* -R <AVG|PROD|MAJ|MIN|MAX|MED>
* The combination rule to use
* (default: AVG)
*
*
* BibTeX:
*
* @book{Kuncheva2004,
* author = {Ludmila I. Kuncheva},
* publisher = {John Wiley and Sons, Inc.},
* title = {Combining Pattern Classifiers: Methods and Algorithms},
* year = {2004}
* }
*
* @article{Kittler1998,
* author = {J. Kittler and M. Hatef and Robert P.W. Duin and J. Matas},
* journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
* number = {3},
* pages = {226-239},
* title = {On combining classifiers},
* volume = {20},
* year = {1998}
* }
*
*
*
* @author Alexander K. Seewald ([email protected])
* @author Eibe Frank ([email protected])
* @author Roberto Perdisci ([email protected])
* @version $Revision: 9093 $
*/
public class Vote
extends RandomizableMultipleClassifiersCombiner
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -637891196294399624L;
/** combination rule: Average of Probabilities */
public static final int AVERAGE_RULE = 1;
/** combination rule: Product of Probabilities (only nominal classes) */
public static final int PRODUCT_RULE = 2;
/** combination rule: Majority Voting (only nominal classes) */
public static final int MAJORITY_VOTING_RULE = 3;
/** combination rule: Minimum Probability */
public static final int MIN_RULE = 4;
/** combination rule: Maximum Probability */
public static final int MAX_RULE = 5;
/** combination rule: Median Probability (only numeric class) */
public static final int MEDIAN_RULE = 6;
/** combination rules */
public static final Tag[] TAGS_RULES = {
new Tag(AVERAGE_RULE, "AVG", "Average of Probabilities"),
new Tag(PRODUCT_RULE, "PROD", "Product of Probabilities"),
new Tag(MAJORITY_VOTING_RULE, "MAJ", "Majority Voting"),
new Tag(MIN_RULE, "MIN", "Minimum Probability"),
new Tag(MAX_RULE, "MAX", "Maximum Probability"),
new Tag(MEDIAN_RULE, "MED", "Median")
};
/** Combination Rule variable */
protected int m_CombinationRule = AVERAGE_RULE;
/** the random number generator used for breaking ties in majority voting
* @see #distributionForInstanceMajorityVoting(Instance) */
protected Random m_Random;
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Class for combining classifiers. Different combinations of "
+ "probability estimates for classification are available.\n\n"
+ "For more information see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Enumeration enm;
Vector result;
result = new Vector();
enm = super.listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
result.addElement(new Option(
"\tThe combination rule to use\n"
+ "\t(default: AVG)",
"R", 1, "-R " + Tag.toOptionList(TAGS_RULES)));
return result.elements();
}
/**
* Gets the current settings of Vote.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String [] getOptions() {
int i;
Vector result;
String[] options;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
result.add("-R");
result.add("" + getCombinationRule());
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Parses a given list of options.
*
* Valid options are:
*
* -S <num>
* Random number seed.
* (default 1)
*
* -B <classifier specification>
* Full class name of classifier to include, followed
* by scheme options. May be specified multiple times.
* (default: "weka.classifiers.rules.ZeroR")
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
* -R <AVG|PROD|MAJ|MIN|MAX|MED>
* The combination rule to use
* (default: AVG)
*
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String tmpStr;
tmpStr = Utils.getOption('R', options);
if (tmpStr.length() != 0)
setCombinationRule(new SelectedTag(tmpStr, TAGS_RULES));
else
setCombinationRule(new SelectedTag(AVERAGE_RULE, TAGS_RULES));
super.setOptions(options);
}
/**
* 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.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
TechnicalInformation additional;
result = new TechnicalInformation(Type.BOOK);
result.setValue(Field.AUTHOR, "Ludmila I. Kuncheva");
result.setValue(Field.TITLE, "Combining Pattern Classifiers: Methods and Algorithms");
result.setValue(Field.YEAR, "2004");
result.setValue(Field.PUBLISHER, "John Wiley and Sons, Inc.");
additional = result.add(Type.ARTICLE);
additional.setValue(Field.AUTHOR, "J. Kittler and M. Hatef and Robert P.W. Duin and J. Matas");
additional.setValue(Field.YEAR, "1998");
additional.setValue(Field.TITLE, "On combining classifiers");
additional.setValue(Field.JOURNAL, "IEEE Transactions on Pattern Analysis and Machine Intelligence");
additional.setValue(Field.VOLUME, "20");
additional.setValue(Field.NUMBER, "3");
additional.setValue(Field.PAGES, "226-239");
return result;
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// class
if ( (m_CombinationRule == PRODUCT_RULE)
|| (m_CombinationRule == MAJORITY_VOTING_RULE) ) {
result.disableAllClasses();
result.disableAllClassDependencies();
result.enable(Capability.NOMINAL_CLASS);
result.enableDependency(Capability.NOMINAL_CLASS);
}
else if (m_CombinationRule == MEDIAN_RULE) {
result.disableAllClasses();
result.disableAllClassDependencies();
result.enable(Capability.NUMERIC_CLASS);
result.enableDependency(Capability.NUMERIC_CLASS);
}
return result;
}
/**
* Buildclassifier selects a classifier from the set of classifiers
* by minimising error on the training data.
*
* @param data the training data to be used for generating the
* boosted classifier.
* @throws Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
m_Random = new Random(getSeed());
for (int i = 0; i < m_Classifiers.length; i++) {
getClassifier(i).buildClassifier(newData);
}
}
/**
* Classifies the given test instance.
*
* @param instance the instance to be classified
* @return the predicted most likely class for the instance or
* Instance.missingValue() if no prediction is made
* @throws Exception if an error occurred during the prediction
*/
public double classifyInstance(Instance instance) throws Exception {
double result;
double[] dist;
int index;
switch (m_CombinationRule) {
case AVERAGE_RULE:
case PRODUCT_RULE:
case MAJORITY_VOTING_RULE:
case MIN_RULE:
case MAX_RULE:
dist = distributionForInstance(instance);
if (instance.classAttribute().isNominal()) {
index = Utils.maxIndex(dist);
if (dist[index] == 0)
result = Instance.missingValue();
else
result = index;
}
else if (instance.classAttribute().isNumeric()){
result = dist[0];
}
else {
result = Instance.missingValue();
}
break;
case MEDIAN_RULE:
result = classifyInstanceMedian(instance);
break;
default:
throw new IllegalStateException("Unknown combination rule '" + m_CombinationRule + "'!");
}
return result;
}
/**
* Classifies the given test instance, returning the median from all
* classifiers.
*
* @param instance the instance to be classified
* @return the predicted most likely class for the instance or
* Instance.missingValue() if no prediction is made
* @throws Exception if an error occurred during the prediction
*/
protected double classifyInstanceMedian(Instance instance) throws Exception {
double[] results = new double[m_Classifiers.length];
double result;
for (int i = 0; i < results.length; i++)
results[i] = m_Classifiers[i].classifyInstance(instance);
if (results.length == 0)
result = 0;
else if (results.length == 1)
result = results[0];
else
result = Utils.kthSmallestValue(results, results.length / 2);
return result;
}
/**
* Classifies a given instance using the selected combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified
* successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double[] result = new double[instance.numClasses()];
switch (m_CombinationRule) {
case AVERAGE_RULE:
result = distributionForInstanceAverage(instance);
break;
case PRODUCT_RULE:
result = distributionForInstanceProduct(instance);
break;
case MAJORITY_VOTING_RULE:
result = distributionForInstanceMajorityVoting(instance);
break;
case MIN_RULE:
result = distributionForInstanceMin(instance);
break;
case MAX_RULE:
result = distributionForInstanceMax(instance);
break;
case MEDIAN_RULE:
result[0] = classifyInstance(instance);
break;
default:
throw new IllegalStateException("Unknown combination rule '" + m_CombinationRule + "'!");
}
if (!instance.classAttribute().isNumeric() && (Utils.sum(result) > 0))
Utils.normalize(result);
return result;
}
/**
* Classifies a given instance using the Average of Probabilities
* combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified
* successfully
*/
protected double[] distributionForInstanceAverage(Instance instance) throws Exception {
double[] probs = getClassifier(0).distributionForInstance(instance);
probs = (double[])probs.clone();
for (int i = 1; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
for (int j = 0; j < dist.length; j++) {
probs[j] += dist[j];
}
}
for (int j = 0; j < probs.length; j++) {
probs[j] /= (double)m_Classifiers.length;
}
return probs;
}
/**
* Classifies a given instance using the Product of Probabilities
* combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified
* successfully
*/
protected double[] distributionForInstanceProduct(Instance instance) throws Exception {
double[] probs = getClassifier(0).distributionForInstance(instance);
probs = (double[])probs.clone();
for (int i = 1; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
for (int j = 0; j < dist.length; j++) {
probs[j] *= dist[j];
}
}
return probs;
}
/**
* Classifies a given instance using the Majority Voting combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified
* successfully
*/
protected double[] distributionForInstanceMajorityVoting(Instance instance) throws Exception {
double[] probs = new double[instance.classAttribute().numValues()];
double[] votes = new double[probs.length];
for (int i = 0; i < m_Classifiers.length; i++) {
probs = getClassifier(i).distributionForInstance(instance);
int maxIndex = 0;
for(int j = 0; j probs[maxIndex])
maxIndex = j;
}
// Consider the cases when multiple classes happen to have the same probability
for (int j=0; j votes[tmpMajorityIndex])
tmpMajorityIndex = k;
}
// Consider the cases when multiple classes receive the same amount of votes
Vector majorityIndexes = new Vector();
for (int k = 0; k < votes.length; k++) {
if (votes[k] == votes[tmpMajorityIndex])
majorityIndexes.add(k);
}
// Resolve the ties according to a uniform random distribution
int majorityIndex = majorityIndexes.get(m_Random.nextInt(majorityIndexes.size()));
//set probs to 0
probs = new double[probs.length];
probs[majorityIndex] = 1; //the class that have been voted the most receives 1
return probs;
}
/**
* Classifies a given instance using the Maximum Probability combination rule.
*
* @param instance the instance to be classified
* @return the distribution
* @throws Exception if instance could not be classified
* successfully
*/
protected double[] distributionForInstanceMax(Instance instance) throws Exception {
double[] max = getClassifier(0).distributionForInstance(instance);
max = (double[])max.clone();
for (int i = 1; i < m_Classifiers.length; i++) {
double[] dist = getClassifier(i).distributionForInstance(instance);
for (int j = 0; j < dist.length; j++) {
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