title: Adding weka instances after classification but before evaluation?
body: <p>Suppose <code>X</code> is a raw, labeled (ie, with training labels) data set, and <code>Process(X)</code> returns a set of <code>Y</code> instances
  that have been encoded with attributes and converted into a weka-friendly file like Y.arff.</p>
  
  <p>Also suppose <code>Process()</code> has some 'leakage':
  some instances <code>Leak = X-Y</code> can't be encoded consistently, and need
  to get a default classification <code>FOO</code>.  The training labels are also known for the Leak set.</p>
  
  <p>My question is how I can best introduce instances from Leak into the
  weka evaluation stream AFTER some classifier has been applied to the
  subset <code>Y</code>, folding the <code>Leak</code> instances in with their default
  classification label, before performing evaulation across the full set <code>X</code>?  In code:</p>
  
  <pre><code>DataSource LeakSrc = new DataSource("leak.arff");
  Instances Leak = LeakSrc.getDataSet();  
  DataSource Ysrc = new DataSource("Y.arff");
  Instances Y = Ysrc.getDataSet();  
  classfr.buildClassifer(Y)
  // YunionLeak = ??
  eval.crossValidateModel(classfr, YunionLeak);
  </code></pre>
  
  <p>Maybe this is a specific example of folding together results
  from multiple classifiers?</p>