/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.tree;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.LearnerCapability;
/**
* This operator learns decision trees without pruning using both nominal
* and numerical attributes.
* Decision trees are powerful classification methods which often can also
* easily be understood. This decision tree learner works similar to Quinlan's
* ID3.
*
* @author Ingo Mierswa
* @version $Id: ID3NumericalLearner.java,v 1.3 2008/05/09 19:22:52 ingomierswa Exp $
*/
public class ID3NumericalLearner extends AbstractTreeLearner {
public ID3NumericalLearner(OperatorDescription description) {
super(description);
}
public Pruner getPruner() throws OperatorException {
return null;
}
public List<Terminator> getTerminationCriteria(ExampleSet exampleSet) {
List<Terminator> result = new LinkedList<Terminator>();
result.add(new SingleLabelTermination());
result.add(new NoAttributeLeftTermination());
result.add(new EmptyTermination());
result.add(new MaxDepthTermination(exampleSet.size()));
return result;
}
public boolean supportsCapability(LearnerCapability capability) {
if (capability == com.rapidminer.operator.learner.LearnerCapability.BINOMINAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.POLYNOMINAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.NUMERICAL_ATTRIBUTES)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.POLYNOMINAL_CLASS)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.BINOMINAL_CLASS)
return true;
if (capability == com.rapidminer.operator.learner.LearnerCapability.WEIGHTED_EXAMPLES)
return true;
return false;
}
}