weka.attributeSelection.InfoGainAttributeEval Java Examples

The following examples show how to use weka.attributeSelection.InfoGainAttributeEval. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: ModelFactory.java    From AIDR with GNU Affero General Public License v3.0 6 votes vote down vote up 
private static AttributeSelection getAttributeSelector(
		Instances trainingData) throws Exception {
	AttributeSelection selector = new AttributeSelection();
	InfoGainAttributeEval evaluator = new InfoGainAttributeEval();
	Ranker ranker = new Ranker();
	ranker.setNumToSelect(Math.min(500, trainingData.numAttributes() - 1));
	selector.setEvaluator(evaluator);
	selector.setSearch(ranker);
	selector.SelectAttributes(trainingData);
	return selector;
}
Example #2
Source File: KddCup.java    From Machine-Learning-in-Java with MIT License 4 votes vote down vote up 
public static Instances preProcessData(Instances data) throws Exception{
	
	/* 
	 * Remove useless attributes
	 */
	RemoveUseless removeUseless = new RemoveUseless();
	removeUseless.setOptions(new String[] { "-M", "99" });	// threshold
	removeUseless.setInputFormat(data);
	data = Filter.useFilter(data, removeUseless);

	
	/* 
	 * Remove useless attributes
	 */
	ReplaceMissingValues fixMissing = new ReplaceMissingValues();
	fixMissing.setInputFormat(data);
	data = Filter.useFilter(data, fixMissing);
	

	/* 
	 * Remove useless attributes
	 */
	Discretize discretizeNumeric = new Discretize();
	discretizeNumeric.setOptions(new String[] {
			"-O",
			"-M",  "-1.0", 
			"-B",  "4",  // no of bins
			"-R",  "first-last"}); //range of attributes
	fixMissing.setInputFormat(data);
	data = Filter.useFilter(data, fixMissing);

	/* 
	 * Select only informative attributes
	 */
	InfoGainAttributeEval eval = new InfoGainAttributeEval();
	Ranker search = new Ranker();
	search.setOptions(new String[] { "-T", "0.001" });	// information gain threshold
	AttributeSelection attSelect = new AttributeSelection();
	attSelect.setEvaluator(eval);
	attSelect.setSearch(search);
	
	// apply attribute selection
	attSelect.SelectAttributes(data);
	
	// remove the attributes not selected in the last run
	data = attSelect.reduceDimensionality(data);
	
	

	return data;
}