#### IMPLEMENTING DECISIONS IN BINARY DECISION TREES USING INDEPENDENT COMPONENT ANALYSIS

Petteri Pajunen

There are various ways to implement decisions in bina
ry decision trees. Most approaches can be interpreted
as greedy methods optimizing some local goodness cri
teria at each node. Often it is required that either
target values (regression trees) or class labels (classi
fication trees) are available. In this paper linear ICA
is applied to implement the decisions in a binary tree.
The rationale is that ICA can be used to find directions
where the data has ``structure''. The linear transforma
tion defined by ICA can then be interpreted as a change
of variables, where the new variable captures the struc
ture, e.g. has smallest entropy. The linear decisions are
then made by tresholding the variable. An experiment
is presented which shows that the proposed method can
find reasonable representations of realworld data in an
unsupervised manner, i.e. without using class labels.