Trainable Weka Segmentation: update fastRandomForest package to version 0.99 by Frank Supek.

--- VERSION 0.99 ---

Multiple speed and memory optimizations, for more details, please refer to
javadoc of the following:
- in the class FastRandomTree, please see function buildTree(), and the new
functions splitDataNew() and distributionSequentialAtt()
- in the FastRfBagging, see function computeOOBError( DataCache data, ... ) and
its helper class VotesCollectorDataCache, and also the buildClassifier() where
the feature importances are computed

Nominal attributes with more than 2 levels are treated differently than before;
in 0.98 and previous, as well as Weka's Random Forest, for such attributes,
splits with many branches are created. In 0.99, all splits are binary, and the
best value of the nominal attribute (in a one-vs-rest fashion) is chosen for the
split. Thus, the accuracy of 0.99 in datasets with multilevel nominal attributes
may differ somewhat (in either direction) from Weka's standard implementation.

The feature to compute RF attribute importances is accessible via the Explorer
GUI and is off by default, as it incurs a significant computational cost.
Results of the feature importance calculation are now displayed in the GUI; they
are output by the FastRandomForest toString() function.

Small changes in the output format of the Benchmark class.
Trainable Weka Segmentation: delete redundant log messages.