Environmental Engineering Reference
In-Depth Information
Chapter 14
Decision Trees in Ecological Modelling
Marko Debeljak and Sa
ˇ
oD
ˇ
eroski
Abstract Decision tree learning is among the most popular machine learning
techniques used for ecological modelling. Decision trees can be used to predict
the value of one or several target (dependent) variables. They are hierarchical
structures, where each internal node contains a test on an attribute, each branch
corresponding to an outcome of the test, and each leaf node giving a prediction for
the value of the class variable. Depending on whether we are dealing with a
classification (discrete target) or a regression problem (continuous target), the
decision tree is called a classification or a regression tree, respectively. The
common way to induce decision trees is the so-called Top-Down Induction of
Decision Tress (TDIDT). In this chapter, we introduce different types of decision
trees, present basic algorithms to learn them, and give an overview of their
applications in ecological modelling. The applications include modelling popula-
tion dynamics and habitat suitability for different organisms (e.g. soil fauna, red
deer, brown bears, bark beetles) in different ecosystems (e.g. aquatic, arable and
forest ecosystems) exposed to different environmental pressures (e.g. agriculture,
forestry, pollution, global warming).
14.1
Introduction
Machine learning
is one of the most essential and active research areas in the field
of artificial intelligence. In short, it is the study of computer programmes that
automatically improve with experience (Mitchell 1997). The most widely investi-
gated type of machine learning is inductive machine learning, where the experience
is given in the form of learning examples. Supervised inductive machine learning,
sometimes also called
predictive modelling
, assumes that each learning example
includes some target property, which should be predicted. The final goal is then to
