  In many data mining applications collection of sufficiently large datasets is
the most time consuming and expensive. On the other hand, industrial methods of
data collection create huge databases, and make difficult direct applications
of the advanced machine learning algorithms. To address the above problems, we
consider active learning (AL), which may be very efficient either for the
experimental design or for the data filtering. In this paper we demonstrate
using the online evaluation opportunity provided by the AL Challenge that quite
competitive results may be produced using a small percentage of the available
data. Also, we present several alternative criteria, which may be useful for
the evaluation of the active learning processes. The author of this paper
attended special presentation in Barcelona, where results of the WCCI 2010 AL
Challenge were discussed.
