Data generator based on RBF network

There are plenty of problems where the data available is scarce and expensive. We propose a generator of semi-artificial data with sim ilar 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. Learned Gaussian kernels can be used in a generative mode to generate the data from the same distributions. To asses quality of the generated data we developed several workflows and used them to evaluate the statistical p roperties of the generated data, structural similarity and predictive similarit y 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.