Automating the Design of Data Mining Algorithms
2:00 pm - 3:00 pm
|Location||St Johns Building - Room 008|
Although there are already many types of data mining algorithms available in the literature, it is still difficult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining algorithms have been manually designed; therefore they incorporate human biases and preferences. In this talk we propose a new approach to the design of data mining algorithms. Instead of relying on the slow and ad hoc process of manual algorithm design, we propose systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic programming system (a type of evolutionary computation method that evolves computer programs) to automate the design of rule induction algorithms, a type of classification method that discovers a set of classification rules from data. We will described the proposed system and show some computational results evaluating its effectiveness.
About the Speaker
Dr. Alex Freitas is a Reader in Computational Intelligence at the University of Kent, UK. He has obtained his PhD in Computer from the University of Essex, UK, in 1997. He has been doing research in the area of data mining with biologically-inspired algorithms for about 15 years. He is the member of the editorial board of 4 international journals, and his publications include 3 research-oriented authored books on data mining, more than 40 peer-reviewed journal papers and more than 100 peer-reviewed papers published in the proceedings of conferences or workshops. He has completed the (co)-supervision of 12 PhD students and has 3 more in training. At present his main research interests are data mining, the biology of ageing, bioinformatics and biologically-inspired computational intelligence methods.