| [4] | 1 | /* |
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| 2 | * This program is free software; you can redistribute it and/or modify |
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| 3 | * it under the terms of the GNU General Public License as published by |
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| 4 | * the Free Software Foundation; either version 2 of the License, or |
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| 5 | * (at your option) any later version. |
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| 6 | * |
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| 7 | * This program is distributed in the hope that it will be useful, |
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| 8 | * but WITHOUT ANY WARRANTY; without even the implied warranty of |
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| 9 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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| 10 | * GNU General Public License for more details. |
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| 11 | * |
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| 12 | * You should have received a copy of the GNU General Public License |
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| 13 | * along with this program; if not, write to the Free Software |
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| 14 | * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. |
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| 15 | */ |
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| 16 | |
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| 17 | /* |
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| 18 | * RELEASE INFORMATION (December 27, 2004) |
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| 19 | * |
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| 20 | * FCBF algorithm: |
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| 21 | * Template obtained from Weka |
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| 22 | * Developped for Weka by Zheng Alan Zhao |
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| 23 | * December 27, 2004 |
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| 24 | * |
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| 25 | * FCBF algorithm is a feature selection method based on Symmetrical Uncertainty |
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| 26 | * Measurement for relevance redundancy analysis. The details of FCBF algorithm are |
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| 27 | * in L. Yu and H. Liu. Feature selection for high-dimensional data: a fast |
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| 28 | * correlation-based filter solution. In Proceedings of the twentieth International |
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| 29 | * Conference on Machine Learning, pages 856--863, 2003. |
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| 30 | * |
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| 31 | * |
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| 32 | * CONTACT INFORMATION |
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| 33 | * |
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| 34 | * For algorithm implementation: |
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| 35 | * Zheng Zhao: zhaozheng at asu.edu |
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| 36 | * |
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| 37 | * For the algorithm: |
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| 38 | * Lei Yu: leiyu at asu.edu |
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| 39 | * Huan Liu: hliu at asu.edu |
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| 40 | * |
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| 41 | * Data Mining and Machine Learning Lab |
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| 42 | * Computer Science and Engineering Department |
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| 43 | * Fulton School of Engineering |
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| 44 | * Arizona State University |
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| 45 | * Tempe, AZ 85287 |
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| 46 | * |
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| 47 | * AttributeSetEvaluator.java |
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| 48 | * |
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| 49 | * Copyright (C) 2004 Data Mining and Machine Learning Lab, |
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| 50 | * Computer Science and Engineering Department, |
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| 51 | * Fulton School of Engineering, |
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| 52 | * Arizona State University |
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| 53 | * |
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| 54 | */ |
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| 55 | |
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| 56 | package weka.attributeSelection; |
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| 57 | |
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| 58 | |
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| 59 | /** |
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| 60 | * Abstract attribute set evaluator. |
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| 61 | * |
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| 62 | * @author Zheng Zhao: zhaozheng at asu.edu |
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| 63 | * @version $Revision: 1.3 $ |
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| 64 | */ |
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| 65 | public abstract class AttributeSetEvaluator extends ASEvaluation { |
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| 66 | |
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| 67 | /** for serialization */ |
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| 68 | private static final long serialVersionUID = -5744881009422257389L; |
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| 69 | |
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| 70 | // =============== |
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| 71 | // Public methods. |
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| 72 | // =============== |
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| 73 | |
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| 74 | /** |
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| 75 | * evaluates an individual attribute |
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| 76 | * |
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| 77 | * @param attribute the index of the attribute to be evaluated |
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| 78 | * @return the "merit" of the attribute |
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| 79 | * @exception Exception if the attribute could not be evaluated |
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| 80 | */ |
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| 81 | public abstract double evaluateAttribute(int attribute) throws Exception; |
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| 82 | |
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| 83 | /** |
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| 84 | * Evaluates a set of attributes |
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| 85 | * |
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| 86 | * @param attributes an <code>int[]</code> value |
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| 87 | * @param classAttributes an <code>int[]</code> value |
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| 88 | * @return a <code>double</code> value |
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| 89 | * @exception Exception if an error occurs |
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| 90 | */ |
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| 91 | public abstract double evaluateAttribute(int[] attributes, int[] classAttributes) |
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| 92 | throws Exception; |
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| 93 | } |
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