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Data Mining using Genetic Programming: Classification and Symbolic Regression
Institute for Programming research and Algorithmics, Leiden Institute of Advanced Computer Science, Faculty of Mathematics & Natural Sciences, Leiden University, The Netherlands, (14 September 2005)
Аннотация
Sir Francis Bacon said about four centuries ago:
"Knowledge is Power". If we look at today's
society, information is becoming increasingly
important. According to 73 about five exabytes (5 �
1018 bytes) of new information were produced in 2002,
92% of which on magnetic media (e.g., hard-disks). This
was more than double the amount of information produced
in 1999 (2 exabytes). However, as Albert Einstein
observed: "Information is not Knowledge".
One of the challenges of the large amounts of
information stored in databases is to find or extract
potentially useful, understandable and novel patterns
in data which can lead to new insights. To quote T.S.
Eliot: "Where is the knowledge we have lost in
information ?" 35. This is the goal of a process
called Knowledge Discovery in Databases (KDD) 36. The
KDD process consists of several phases: in the Data
Mining phase the actual discovery of new knowledge
takes place.
The outline of the rest of this introduction is as
follows. We start with an introduction of Data Mining
and more specifically the two subject areas of Data
Mining we will be looking at: classification and
regression. Next we give an introduction about
evolutionary computation in general and tree-based
genetic programming in particular. In Section 1.4 we
give our motivation for using genetic programming for
Data Mining. Finally, in the last sections we give an
overview of the thesis and related publications.
