source: src/main/java/weka/attributeSelection/AttributeSetEvaluator.java @ 19

Last change on this file since 19 was 4, checked in by gnappo, 14 years ago

Import di weka.

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