
@Title(value="Feature Bagging for Outlier Detection") @Reference(title="Feature Bagging for Outlier Detection", authors="A. Lazarevic, V. Kumar", booktitle="Proc. of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining", url="http://dx.doi.org/10.1145/1081870.1081891") public class FeatureBagging extends AbstractAlgorithm<OutlierResult> implements OutlierAlgorithm
Since the proposed method is only sensible to run on multiple instances of the same algorithm (due to incompatible score ranges), we do not allow using arbitrary algorithms.
 Reference: 
 A. Lazarevic, V. Kumar: Feature Bagging for Outlier Detection
 In: Proc. of the 11th ACM SIGKDD international conference on Knowledge
 discovery in data mining
 
| Modifier and Type | Class and Description | 
|---|---|
| static class  | FeatureBagging.ParameterizerParameterization class. | 
| Modifier and Type | Field and Description | 
|---|---|
| protected boolean | breadthCumulative sum or breadth first combinations | 
| private int | kThe parameters k for LOF. | 
| private static Logging | loggerThe logger for this class. | 
| protected int | numNumber of instances to use | 
| private Random | RANDOMRandom number generator for subspace choice | 
| Constructor and Description | 
|---|
| FeatureBagging(int k,
              int num,
              boolean breadth,
              Long seed)Constructor. | 
| Modifier and Type | Method and Description | 
|---|---|
| TypeInformation[] | getInputTypeRestriction()Get the input type restriction used for negotiating the data query. | 
| protected Logging | getLogger()Get the (STATIC) logger for this class. | 
| private BitSet | randomSubspace(int alldim,
              int mindim,
              int maxdim)Choose a random subspace | 
| OutlierResult | run(Relation<NumberVector<?,?>> relation)Run the algorithm on a data set. | 
makeParameterDistanceFunction, runclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitrunprivate static final Logging logger
protected int num
protected boolean breadth
private Random RANDOM
private int k
public FeatureBagging(int k,
              int num,
              boolean breadth,
              Long seed)
k - k Parameter for LOFnum - Number of subspaces to usebreadth - Flag for breadth-first mergingpublic OutlierResult run(Relation<NumberVector<?,?>> relation)
relation - Relation to useprivate BitSet randomSubspace(int alldim, int mindim, int maxdim)
alldim - Number of total dimensionsmindim - Minimum number to choosemaxdim - Maximum number to chooseprotected Logging getLogger()
AbstractAlgorithmgetLogger in class AbstractAlgorithm<OutlierResult>public TypeInformation[] getInputTypeRestriction()
AbstractAlgorithmgetInputTypeRestriction in interface AlgorithmgetInputTypeRestriction in class AbstractAlgorithm<OutlierResult>