Fuzzy Decision Trees - Data Mining with Decision Trees: Theory and Applications - page 235

(1992) use the ID3 algorithm to select effective numerical attributes. The
obtained splitting intervals are used as fuzzy boundaries. Regression is then
used in each sub-space to form fuzzy rules. Cios and Sztandera (1992) use
the ID3 algorithm to convert a decision tree into a layer of a feedforward
neural network. Each neuron is represented as a hyperplane with a fuzzy
boundary. The nodes within the hidden layer are generated until some fuzzy
entropy is reduced to zero. New hidden layers are generated until there is
only one node at the output layer.
Fuzzy-CART [ Jang (1994) ] is a method which uses the CART algorithm
to build a tree. However, the tree, which is the first step, is only used
to propose fuzzy sets of the continuous domains (using the generated
thresholds). Then, a layered network algorithm is employed to learn fuzzy
rules. This produces more comprehensible fuzzy rules and improves the
CART's initial results.
Another complete framework for building a fuzzy tree including several
inference procedures based on conflict resolution in rule-based systems and
ecient approximate reasoning methods was presented in [ Janikow, 1998 ] .
Olaru and Wehenkel (2003) presented a new type of fuzzy decision
trees called soft decision trees (SDT). This approach combines tree-growing
and pruning, to determine the structure of the soft decision tree. Refitting
and backfitting are ised to improve its generalization capabilities. The
researchers empirically showed that soft decision trees are significantly more
accurate than standard decision trees. Moreover, a global model variance
study shows a much lower variance for soft decision trees than for standard
trees as a direct cause of the improved accuracy.
Peng (2004) has used FDT to improve the performance of the classical
inductive learning approach in manufacturing processes. Peng proposed
using soft discretization of continuous-valued attributes. It has been shown
that FDT can deal with the noise or uncertainties existing in the data
collected in industrial systems.