| Abstract: |  In the areas of Data Mining (DM) and Knowledge Discovery (KD), a large variety of algorithms has been developed in the past decades, and the research is still ongoing. Moreover, there is a large amount of data being stored continuously into databases all over the world. These data contain a lot of useful knowledge, which would be very beneficial to extract. However, an assistance of a human expert is usually needed in order to employ the algorithms available. Specifically, a process of interconnected actions referred to as knowledge ow needs to be assembled when the algorithms are to applied on a given data. This calls for researching approaches to automated process creation. In this thesis, an innovative evolutionary algorithm for automated knowledge discovery
process construction is designed and tested on different datasets. Moreover, an investigation of unconventional interactive evolutionary approach is done, followed by several experiments. It is shown that human feedback is an interesting alternative to traditional concept of automatically evaluated fitness.
|
|---|