/* * 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 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 General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. */ /* * RELEASE INFORMATION (December 27, 2004) * * FCBF algorithm: * Template obtained from Weka * Developped for Weka by Zheng Alan Zhao * December 27, 2004 * * FCBF algorithm is a feature selection method based on Symmetrical Uncertainty * Measurement for relevance redundancy analysis. The details of FCBF algorithm are * in L. Yu and H. Liu. Feature selection for high-dimensional data: a fast * correlation-based filter solution. In Proceedings of the twentieth International * Conference on Machine Learning, pages 856--863, 2003. * * * CONTACT INFORMATION * * For algorithm implementation: * Zheng Zhao: zhaozheng at asu.edu * * For the algorithm: * Lei Yu: leiyu at asu.edu * Huan Liu: hliu at asu.edu * * Data Mining and Machine Learning Lab * Computer Science and Engineering Department * Fulton School of Engineering * Arizona State University * Tempe, AZ 85287 * * AttributeSetEvaluator.java * * Copyright (C) 2004 Data Mining and Machine Learning Lab, * Computer Science and Engineering Department, * Fulton School of Engineering, * Arizona State University * */ package weka.attributeSelection; /** * Abstract attribute set evaluator. * * @author Zheng Zhao: zhaozheng at asu.edu * @version $Revision: 8034 $ */ public abstract class AttributeSetEvaluator extends ASEvaluation { /** for serialization */ private static final long serialVersionUID = -5744881009422257389L; // =============== // Public methods. // =============== /** * evaluates an individual attribute * * @param attribute the index of the attribute to be evaluated * @return the "merit" of the attribute * @exception Exception if the attribute could not be evaluated */ public abstract double evaluateAttribute(int attribute) throws Exception; /** * Evaluates a set of attributes * * @param attributes an <code>int[]</code> value * @param classAttributes an <code>int[]</code> value * @return a <code>double</code> value * @exception Exception if an error occurs */ public abstract double evaluateAttribute(int[] attributes, int[] classAttributes) throws Exception; }