继续weka的编程系列。数据挖掘的一个重要的过程就是要特征选择，主要作用就是降维，并且降低计算的复杂性，摒弃那些可能的潜在噪声。在我的paper中和硕士论文中都用到了CFS的特征子集选择方法，配以最佳优先的搜索或者贪心搜索，这样可以将维度比较高的训练特征集降维并简化，大概用CFS+Best first可以将我的训练样本中的145维特征降到40-50之间。
具体的实现方法见下面的测试代码（只做示范用）：
其中的注释部分是使用交叉验证的部分。默认是十折交叉验证，当然这个可以通过set方法设置。具体的使用或者用到reduce dimensionality的方法大家可以参看源代码。毕竟weka开源很是方便。源代码涉及到的类主要是查看weka.attributeSelection.AttributeSelection类就可以了。当然如何调用和选择可以看看weka.gui.explorer.AttributeSelectionPanel类。
上面代码的实验结果如下：
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具体的实现方法见下面的测试代码（只做示范用）：
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/**
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*
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*/
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package edu.tju.ikse.mi.util;
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import java.io.File;
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import java.io.IOException;
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import java.util.Random;
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import weka.attributeSelection.ASEvaluation;
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import weka.attributeSelection.ASSearch;
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import weka.attributeSelection.AttributeSelection;
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import weka.attributeSelection.BestFirst;
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import weka.attributeSelection.CfsSubsetEval;
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import weka.core.Instances;
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import weka.core.converters.ArffLoader;
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/**
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* @author Jia Yu
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* @date 2010-11-23
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*/
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public class WekaSelector {
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private ArffLoader loader;
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private Instances dataSet;
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private File arffFile;
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private int sizeOfDataset;
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private int numOfOldAttributes;
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private int numOfNewAttributes;
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private int classIndex;
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private int[] selectedAttributes;
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public WekaSelector(File file) throws IOException {
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loader = new ArffLoader();
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arffFile = file;
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loader.setFile(arffFile);
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dataSet = loader.getDataSet();
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sizeOfDataset = dataSet.numInstances();
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numOfOldAttributes = dataSet.numAttributes();
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classIndex = numOfOldAttributes - 1;
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dataSet.setClassIndex(classIndex);
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}
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public void select() throws Exception {
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ASEvaluation evaluator = new CfsSubsetEval();
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ASSearch search = new BestFirst();
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AttributeSelection eval = null;
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eval = new AttributeSelection();
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eval.setEvaluator(evaluator);
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eval.setSearch(search);
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eval.SelectAttributes(dataSet);
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numOfNewAttributes = eval.numberAttributesSelected();
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selectedAttributes = eval.selectedAttributes();
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System.out.println("result is "+eval.toResultsString());
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/*
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Random random = new Random(seed);
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dataSet.randomize(random);
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if (dataSet.attribute(classIndex).isNominal()) {
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dataSet.stratify(numFolds);
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}
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for (int fold = 0; fold < numFolds; fold++) {
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Instances train = dataSet.trainCV(numFolds, fold, random);
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eval.selectAttributesCVSplit(train);
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}
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System.out.println("result is "+eval.CVResultsString());
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*/
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System.out.println("old number of Attributes is "+numOfOldAttributes);
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System.out.println("new number of Attributes is "+numOfNewAttributes);
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for(int i=0;i<selectedAttributes.length;i++){
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System.out.println(selectedAttributes[i]);
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}
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}
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/**
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* @param args
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*/
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public static void main(String[] args) {
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// TODO Auto-generated method stub
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File file = new File("iris.arff");
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try {
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WekaSelector ws = new WekaSelector(file);
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ws.select();
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} catch (IOException e) {
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// TODO Auto-generated catch block
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e.printStackTrace();
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} catch (Exception e) {
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// TODO Auto-generated catch block
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e.printStackTrace();
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}
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}
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}
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/**
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* 3
*/4
package edu.tju.ikse.mi.util;5
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import java.io.File;7
import java.io.IOException;8
import java.util.Random;9
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import weka.attributeSelection.ASEvaluation;11
import weka.attributeSelection.ASSearch;12
import weka.attributeSelection.AttributeSelection;13
import weka.attributeSelection.BestFirst;14
import weka.attributeSelection.CfsSubsetEval;15
import weka.core.Instances;16
import weka.core.converters.ArffLoader;17
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/**19
* @author Jia Yu20
* @date 2010-11-2321
*/22
public class WekaSelector {
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private ArffLoader loader;25
private Instances dataSet;26
private File arffFile;27
private int sizeOfDataset;28
private int numOfOldAttributes;29
private int numOfNewAttributes;30
private int classIndex;31
private int[] selectedAttributes;32
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public WekaSelector(File file) throws IOException {
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loader = new ArffLoader();35
arffFile = file;36
loader.setFile(arffFile);37
dataSet = loader.getDataSet();38
sizeOfDataset = dataSet.numInstances();39
numOfOldAttributes = dataSet.numAttributes();40
classIndex = numOfOldAttributes - 1;41
dataSet.setClassIndex(classIndex);42
}43
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public void select() throws Exception {45
ASEvaluation evaluator = new CfsSubsetEval();46
ASSearch search = new BestFirst();47
AttributeSelection eval = null;48
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eval = new AttributeSelection();50
eval.setEvaluator(evaluator);51
eval.setSearch(search);52
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eval.SelectAttributes(dataSet);54
numOfNewAttributes = eval.numberAttributesSelected();55
selectedAttributes = eval.selectedAttributes();56
System.out.println("result is "+eval.toResultsString());57
/*58
Random random = new Random(seed);59
dataSet.randomize(random);60
if (dataSet.attribute(classIndex).isNominal()) {61
dataSet.stratify(numFolds);62
}63
for (int fold = 0; fold < numFolds; fold++) {64
Instances train = dataSet.trainCV(numFolds, fold, random);65
eval.selectAttributesCVSplit(train);66
}67
System.out.println("result is "+eval.CVResultsString());68
*/69
System.out.println("old number of Attributes is "+numOfOldAttributes);70
System.out.println("new number of Attributes is "+numOfNewAttributes);71
for(int i=0;i<selectedAttributes.length;i++){72
System.out.println(selectedAttributes[i]);73
}74
}75
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/**77
* @param args78
*/79
public static void main(String[] args) {80
// TODO Auto-generated method stub81
File file = new File("iris.arff");82
try {83
WekaSelector ws = new WekaSelector(file);84
ws.select();85
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} catch (IOException e) {87
// TODO Auto-generated catch block88
e.printStackTrace();89
} catch (Exception e) {90
// TODO Auto-generated catch block91
e.printStackTrace();92
}93
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}95
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}97
其中的注释部分是使用交叉验证的部分。默认是十折交叉验证，当然这个可以通过set方法设置。具体的使用或者用到reduce dimensionality的方法大家可以参看源代码。毕竟weka开源很是方便。源代码涉及到的类主要是查看weka.attributeSelection.AttributeSelection类就可以了。当然如何调用和选择可以看看weka.gui.explorer.AttributeSelectionPanel类。
上面代码的实验结果如下：
result is
=== Attribute Selection on all input data ===
Search Method:
Best first.
Start set: no attributes
Search direction: forward
Stale search after 5 node expansions
Total number of subsets evaluated: 12
Merit of best subset found: 0.887
Attribute Subset Evaluator (supervised, Class (nominal): 5 class):
CFS Subset Evaluator
Including locally predictive attributes
Selected attributes: 3,4 : 2
petallength
petalwidth
old number of Attributes is 5
new number of Attributes is 2
2
3
4
原来的iris数据集中共有4个属性（包含一个分类类标所以一共5维），经过特征选择后，只有第3和第4两个维度的特征保留，所以新特征子集有两个维度（不包含类标，有点绕，不好意思，我总是这样）。
最后的2，3，4是属性数组的下标，表示经过特征选择保留的属性子集是第3，4，5个属性。