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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.
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.
Computational Intelligence (CI) community has developed hundreds of
algorithms for intelligent data analysis, but still many hard
problems in computer vision, signal processing or text and
multimedia understanding, problems that require deep learning
techniques, are open. Modern data mining packages contain numerous
modules for data acquisition, pre-processing, feature selection and
construction, instance selection, classification, association and
approximation methods, optimization techniques, pattern discovery,
clusterization, visualization and post-processing. A large data
mining package allows for billions of ways in which these modules
can be combined. No human expert can claim to explore and
understand all possibilities in the knowledge discovery process.
This is where algorithms that learn how to learnl come to rescue.
Operating in the space of all available data transformations and
optimization techniques these algorithms use meta-knowledge about
learning processes automatically extracted from experience of
solving diverse problems. Inferences about transformations useful
in different contexts help to construct learning algorithms that
can uncover various aspects of knowledge hidden in the data.
Meta-learning shifts the focus of the whole CI field from
individual learning algorithms to the higher level of learning how
to learn. This book defines and reveals new theoretical and
practical trends in meta-learning, inspiring the readers to further
research in this exciting field.
Computational Intelligence (CI) community has developed hundreds of
algorithms for intelligent data analysis, but still many hard
problems in computer vision, signal processing or text and
multimedia understanding, problems that require deep learning
techniques, are open. Modern data mining packages contain numerous
modules for data acquisition, pre-processing, feature selection and
construction, instance selection, classification, association and
approximation methods, optimization techniques, pattern discovery,
clusterization, visualization and post-processing. A large data
mining package allows for billions of ways in which these modules
can be combined. No human expert can claim to explore and
understand all possibilities in the knowledge discovery process.
This is where algorithms that learn how to learnl come to rescue.
Operating in the space of all available data transformations and
optimization techniques these algorithms use meta-knowledge about
learning processes automatically extracted from experience of
solving diverse problems. Inferences about transformations useful
in different contexts help to construct learning algorithms that
can uncover various aspects of knowledge hidden in the data.
Meta-learning shifts the focus of the whole CI field from
individual learning algorithms to the higher level of learning how
to learn. This book defines and reveals new theoretical and
practical trends in meta-learning, inspiring the readers to further
research in this exciting field.
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