MATLAB File Help: prtClassBumping
 prtClassBumping Bumping (Bootstrap Selection) Classifier
     CLASSIFIER = prtClassBumping returns a Bumping classifier
     CLASSIFIER = prtClassBumping(PROPERTY1, VALUE1, ...) constructs a
     prtClassBumping object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
  A prtClassBumping object inherits all properties from the abstract class
  prtClass. In addition is has the following properties:
       baseClassifier    - The base classifier to be re-trained with bootstrap
       nBags             - The number of bagging samples to use
       includeOriginalDataClassifier - Boolean value specifying whether to
                                       include a classifier trained with
                                       all the available data (not a
                                       bootstrap sample) in comparison.
                                       Defaults to "false" since training
                                       with all the available data can
                                       result in over-training.
  After training, a Bump classifier contains a field "Classifier" with the
  best trained classification algorithm, and a vector baggedPerformance
  with the percent correct found for each bagging sample.
  A Bumping classifier is a meta-classifier that chooses one of several
  classifiers trained on a bootstrap sampled version of the input training
  data.  In this case, the classifier chosen is the classifier trained on
  the bootstrap sample that results in the smallest percent error when
  tested on the original data set.  Bumping classifiers can be useful when
  the data set under consideration has a small number of significant
  outliers; some of the bagging samples will be free of at least some of
  the outliers and may provide better generalization performance.
  For more information on Bumping classifiers, see:
   Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
  % Example:
  ds = prtDataGenUnimodal;
  % add a significant outlier to the data:
  ds = ds.setXY(cat(1,ds.getObservations,[-30 -10]),cat(1,ds.getTargets,1));
  fld = prtClassFld('internalDecider',prtDecisionBinaryMinPe);
  fld = fld.train(ds);
  bumpingFld = prtClassBumping('baseClassifier',fld);
  bumpingFld = bumpingFld.train(ds);
  subplot(2,1,1); plot(fld);
  subplot(2,1,2); plot(bumpingFld);
See also
Class Details
Superclasses prtClass
Sealed false
Construct on load false
Constructor Summary
prtClassBumping Bumping (Bootstrap Selection) Classifier 
Property Summary
baseClassifier The classifier to be bagged 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
includeOriginalDataClassifier Whether or not to run one additional classifier with the original data.  
internalDecider Optional prtDecider object for making decisions 
isCrossValidateValid True 
isNativeMary False 
isSupervised True 
isTrained Indicates if prtAction object has been trained. 
nBags The number of bags 
name Bumping 
nameAbbreviation Bumping 
twoClassParadigm Whether the classifier retures one output (binary) or two outputs (m-ary) when there are only two unique class labels 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
Method Summary
  crossValidate Cross validate prtAction using prtDataSet and cross validation keys. 
  get get the object properties 
  kfolds Perform K-folds cross-validation of prtAction 
  optimize Optimize action parameter by exhaustive function maximization. 
  plot Plot the output confidence of a prtClass object 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.