Abstract
Explores a promising data mining approach. Despite the
small number of examples available in the authors'
application domain (taking into account the large
number of attributes), the results of their experiments
can be considered very promising. The discovered rules
had good performance concerning predictive accuracy,
considering both the rule set as a whole and each
individual rule. Furthermore, what is more important
from a data mining viewpoint, the system discovered
some comprehensible rules. It is interesting to note
that the system achieved very consistent results by
working from "tabula rasa," without any background
knowledge, and with a small number of examples. The
authors emphasize that their system is still in an
experiment in the research stage of development.
Therefore, the results presented here should not be
used alone for real-world diagnoses without consulting
a physician. Future research includes a careful
selection of attributes in a preprocessing step, so as
to reduce the number of attributes (and the
corresponding search space) given to the GP. Attribute
selection is a very active research area in data
mining. Given the results obtained so far, GP has been
demonstrated to be a really useful data mining tool,
but future work should also include the application of
the GP system proposed here to other data sets, to
further validate the results reported in this
article.
- accuracy,
- algorithms,
- applications
- background
- chest-pain
- comprehensible
- data
- diagnosis,
- discovery,
- genetic
- knowledge
- knowledge,
- medical
- mining,
- predictive
- preprocessing
- programming,
- rule
- rules,
- set,
- sets,
- step,
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