  There are plenty of problems where the data available is scarce and
expensive. We propose a generator of semi-artificial data with similar
properties to the original data which enables development and testing of
different data mining algorithms and optimization of their parameters. The
generated data allow a large scale experimentation and simulations without
danger of overfitting. The proposed generator is based on RBF networks, which
learn sets of Gaussian kernels. These Gaussian kernels can be used in a
generative mode to generate new data from the same distributions. To assess
quality of the generated data we evaluated the statistical properties of the
generated data, structural similarity and predictive similarity using
supervised and unsupervised learning techniques. To determine usability of the
proposed generator we conducted a large scale evaluation using 51 UCI data
sets. The results show a considerable similarity between the original and
generated data and indicate that the method can be useful in several
development and simulation scenarios. We analyze possible improvements in
classification performance by adding different amounts of generated data to the
training set, performance on high dimensional data sets, and conditions when
the proposed approach is successful.
