Any attribute selection criterion may work well with
some data, and may not work well with other data. This
work try to solve this problem by developing a new
system known as Multi-Criteria Decision Tree (MCDT)
learning system, it allows the decision tree to be learned
using a combination of three attribute selection criteria:
Gain Ratio, Chi_
Square(X2), and Apriori, then the
learned tree is pruned using Expected Error Pruning
algorithm. The user utilizes a parameter to adjust the
pruning process to control the level where the pruning
process takes place. The predictive accuracies of the
decision trees learned using each of the three attribute
selection criteria are calculated and compared with the
new approach for fourteen data sets.
The obtained decision t r e e learned using one of the
implemented attribute selection criteria is visualized to
the user; the visualized decision tree can be relearned
with more than one attribute selection criteria. The user
can modify the decision tree at any node by selecting a
different attribute selection criterion, and reconstructing
the sub-tree branches from the selected node. This
process can be repeated any number of times for each
node.