TITLE: Automating the Design of Data Mining Algorithms with an Evolutionary Algorithm
SPEAKER: Alex Freitas (Kent University)

ABSTRACT:

Decision-tree induction algorithms are one of the most popular types of
classification algorithms in the field of data mining and knowledge
discovery. Research on this type of algorithms produced many new
algorithms in the last 30 years or so. However, all the decision-tree
induction algorithms created over that period have in common the fact that
they have been manually designed, typically by incrementally modifying a
few basic decision-tree induction algorithms. Having these basic
algorithms and their components in mind, we propose an evolutionary
algorithm to automate the process of designing decision-tree induction
algorithms. The basic motivation is to automatically create complete
decision-tree induction algorithms in a data-driven way, trying to avoid
the human biases and preconceptions incorporated in manually-designed
algorithms. The proposed evolutionary algorithm is evaluated on a number
of datasets, and the results show that the machine-designed decision-tree
induction algorithms are very competitive with well-known human-designed
decision-tree algorithms.