Meta-Learning in Decision Tree Induction by Krzysztof GräbczewskiMeta-Learning in Decision Tree Induction by Krzysztof Gräbczewski

Meta-Learning in Decision Tree Induction

byKrzysztof Gräbczewski

Hardcover | September 23, 2013

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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.
Title:Meta-Learning in Decision Tree InductionFormat:HardcoverDimensions:343 pagesPublished:September 23, 2013Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319009591

ISBN - 13:9783319009599

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Table of Contents

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