Meta-Learning in Decision Tree Induction

This book discusses decision tree induction, reviewing techniques used in decision tree learning, and ensemble methods that involve decision trees. The second half reviews meta-learning approaches, including many that provide a ranking of algorithms.

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.

Presents a general meta-learning approach which is applicable to a variety of machine learning algorithms Focuses on different variants of decision tree induction Details the long and complex road from various small and larger algorithms to a unified approach and the robustness of meta-learning Includes supplementary material: sn.pub/extras

Inhalt
Introduction.- Techniques of decision tree induction.- Multivariate decision trees.- Unified view of decision tree induction algorithms.- Intemiadvanced meta-learning framework.- Meta-level analysis of decision tree induction.