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
                           samples
       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
   Theory.
 
  % 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);