The book focuses on different variants of decision tree induction
but also describes the meta-learning approach in general which is
applicable to other types of machine learning algorithms. The book
discusses different variants of decision tree induction and
represents a useful source of information to readers wishing to
review some of the techniques used in decision tree learning, as
well as different ensemble methods that involve decision trees. It
is shown that the knowledge of different components used within
decision tree learning needs to be systematized to enable the
system to generate and evaluate different variants of machine
learning algorithms with the aim of identifying the top-most
performers or potentially the best one. A unified view of decision
tree learning enables to emulate different decision tree algorithms
simply by setting certain parameters. As meta-learning requires
running many different processes with the aim of obtaining
performance results, a detailed description of the experimental
methodology and evaluation framework is provided. Meta-learning is
discussed in great detail in the second half of the book. The
exposition starts by presenting a comprehensive review of many
meta-learning approaches explored in the past described in
literature, including for instance approaches that provide a
ranking of algorithms. The approach described can be related to
other work that exploits planning whose aim is to construct data
mining workflows. The book stimulates interchange of ideas between
different, albeit related, approaches.
General
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