  With the development of Internet of Things (IoT), IoT intelligence becomes
emerging technology. "Curse of Dimensionality" is the barrier of data fusion in
edge devices for the success of IoT intelligence. A Linguistic Attribute
Hierarchy (LAH), embedded with Linguistic Decision Trees (LDTs), can represent
a new attribute deep learning. In contrast to the conventional deep learning,
an LAH could overcome the shortcoming of missing interpretation by providing
transparent information propagation through the rules, produced by LDTs in the
LAH. Similar to the conventional deep learning, the computing complexity of
optimising LAHs blocks the applications of LAHs. In this paper, we propose a
heuristic approach to constructing an LAH, embedded with LDTs for decision
making or classification by utilising the distance correlations between
attributes and between attributes and the goal variable. The set of attributes
is divided to some attribute clusters, and then they are heuristically
organised to form a linguistic attribute hierarchy. The proposed approach was
validated with some benchmark decision making or classification problems from
the UCI machine learning repository. The experimental results show that the
proposed self-organisation algorithm can construct an effective and efficient
linguistic attribute hierarchy. Such a self-organised linguistic attribute
hierarchy embedded with LDTs can not only efficiently tackle "curse of
dimensionality" in a single LDT for data fusion with massive attributes, but
also achieve better or comparable performance on decision making or
classification, compared to the single LDT for the problem to be solved. The
self-organisation algorithm is much efficient than the Genetic Algorithm in
Wrapper for the optimisation of LAHs. This makes it feasible to embed the
self-organisation algorithm in edge devices for IoT intelligence.
