use weka to do Attribute Selection - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

use weka to do Attribute Selection

Demo Code

/*/*from w ww. jav  a 2 s .co  m*/
 *  How to use WEKA API in Java 
 *  Copyright (C) 2014 
 *  @author Dr Noureddin M. Sadawi (noureddin.sadawi@gmail.com)
 *  
 *  This program is free software: you can redistribute it and/or modify
 *  it as you wish ... 
 *  I ask you only, as a professional courtesy, to cite my name, web page 
 *  and my YouTube Channel!
 *  
 */
package weka.api;

//import required classes
import weka.attributeSelection.CfsSubsetEval;
import weka.attributeSelection.GreedyStepwise;
import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.supervised.attribute.AttributeSelection;
import weka.core.converters.ArffSaver;
import java.io.File;
import weka.core.converters.ConverterUtils.DataSource;

public class AttrSelection {
    public static void main(String args[]) throws Exception {
        //load dataset
        DataSource source = new DataSource(
                "/home/likewise-open/ACADEMIC/csstnns/Desktop/qdb1.arff");
        Instances dataset = source.getDataSet();
        //create AttributeSelection object
        AttributeSelection filter = new AttributeSelection();
        //create evaluator and search algorithm objects
        CfsSubsetEval eval = new CfsSubsetEval();
        GreedyStepwise search = new GreedyStepwise();
        //set the algorithm to search backward
        search.setSearchBackwards(true);
        //set the filter to use the evaluator and search algorithm
        filter.setEvaluator(eval);
        filter.setSearch(search);
        //specify the dataset
        filter.setInputFormat(dataset);
        //apply
        Instances newData = Filter.useFilter(dataset, filter);
        //save
        ArffSaver saver = new ArffSaver();
        saver.setInstances(newData);
        saver.setFile(new File(
                "/home/likewise-open/ACADEMIC/csstnns/Desktop/qdb3.arff"));
        saver.writeBatch();
    }
}

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