Weka Development [4]-Feature Selection

 Feature selection, I am not familiar with this part, probably say, withattributeselection for feature selection, it needs to be set 3 Aspects, First: Class for attribute evaluation (self to attribute Evaluator ), second: the Way to search (self to weka See in the software, English search Method ), Third: It is the data set that you want to perform the feature selection. Finally call filter static method < Span style= "font-family: ' Times New Roman ';" >userfilter , feel the writing is nonsense, a look at the code will understand. The only thing worth saying is don't put attributeselection 's package was added incorrectly, There is a comment next to the code.

Another function lazy explanation (it is not I write), is basically self-explanatory, it is unlikely to read.

Package instancetest;

Import Java.io.FileReader;

Import Java.util.Random;

Import Weka.attributeSelection.CfsSubsetEval;

Import weka.attributeSelection.GreedyStepwise;

Import weka.classifiers.Evaluation;

Import Weka.classifiers.meta.AttributeSelectedClassifier;

Import weka.classifiers.trees.J48;

Import weka.core.Instances;

Import Weka.filters.Filter;

Import weka.filters.supervised.attribute.AttributeSelection;

Public class filtertest

{

Private Instances m_instances = null;

Public void getfileinstances (String fileName) throws Exception

{

FileReader frdata = new FileReader (fileName);

m_instances = new instances (frdata);

m_instances. Setclassindex ( m_instances. Numattributes ()-1);

}

Public void selectattusefilter () throws Exception

{

Attributeselection filter = new attributeselection (); //Package weka.filters.supervised.attribute!

Cfssubseteval eval = new cfssubseteval ();

Greedystepwise search = new greedystepwise ();

Filter.setevaluator (eval);

Filter.setsearch (search);

Filter.setinputformat ( m_instances );

System. out . println ( "number of instance attribute =" +m_instances. numattributes ());

Instances selectedins = Filter. Usefilter ( m_instances, filter);

System. out . println ( "Number of Selected instance attribute =" + selectedins.numattributes ());

}

Public void selectattusemc () throws Exception

{

Attributeselectedclassifier classifier = newattributeselectedclassifier ();

Cfssubseteval eval = new cfssubseteval ();

Greedystepwise search = new greedystepwise ();

J48 base = new J48 ();

Classifier.setclassifier (base);

Classifier.setevaluator (eval);

Classifier.setsearch (search);

//10-fold cross-validation

Evaluation Evaluation = new Evaluation ( m_instances );

Evaluation.crossvalidatemodel (classifier, m_instances, newRandom (1));

System. out . println (Evaluation.tosummarystring ());

}

Public static void main (string[] args) throws Exception

{

Filtertest filter = new filtertest ();

Filter.getfileinstances ( "F://program Files//weka-3-4//data//soybean.arff");

Filter.selectattusefilter ();

FILTER.SELECTATTUSEMC ();

}

}

Weka Development [4]-Feature Selection