2008 Final Year Project – 1st Term Report - The Chinese University ...

Department of Computer Science and Engineering, CUHK
2007 – 2008 Final Year Project – 1st Term Report
3.5.1.4.2 Discretization of attributes
Discretization refers to the separation of numeric attributes into a smaller
number of distinct ranges. This idea exists because some classification algorithms
can only deal with nominal attributes only and cannot handle attributes on a
numeric scale [7]. So as to increase the reliability of classification, attributes are
discretized before actual classification starts.
3.5.1.5 PROS AND CONS AMONG CLASSIFIERS
of them:
Among the classifiers we have tested, we got the following conclusion about each
1. When compared to neural networks, tree and rule-based classifiers are easy to
be coded. Weka visualizes the classification tree or rules to the user after
learning is completed. Obviously this makes coding of the decision trees or
rules much easier by just using a number of if statements. However, for neural
networks, it is not easy to have prototype within a second and thus we cannot
use this to construct classifiers easily.
2. From our trials, we found that neural network can always guarantees accuracy
greater than 90%. However, the training time is longer when compare to other
algorithms.
3. For tree or rule-based algorithm, as well as neural networks, in order to have
an accurate result, a large number of data samples must be ready in the
learning step.
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