About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

• A new fast (sublinear in the number of instances) stump algorithm is implemented. The gain in time is proportional to the sparsity of the features (it is significant when a lot of instances take the most frequent feature value). See Section B.2 in the documentation.
• A parametrized early stopping option is added in --traintest mode. We stop if the (smoothed) test error does not improve for a certain number of iterations. See Section 4.1.3 in the documentation.

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Initial Announcement on mloss.org.