Dataset Characteristics (Metafeatures)
Cognitive technologies, pp. 53–75
Abstract
Summary This chapter discusses dataset characteristics that play a crucial role in many metalearning systems. Typically, they help to restrict the search in a given configuration space. The basic characteristic of the target variable, for instance, determines the choice of the right approach. If it is numeric, it suggests that a suitable regression algorithm should be used, while if it is categorical, a classification algorithm should be used instead. This chapter provides an overview of different types of dataset characteristics, which are sometimes also referred to as metafeatures. These are of different types, and include so-called simple, statistical, information-theoretic, model-based, complexitybased, and performance-based metafeatures. The last group of characteristics has the advantage that it can be easily defined in any domain. These characteristics include, for instance,sampling landmarkersrepresenting the performance of particular algorithms on samples of data,relative landmarkerscapturing differences or ratios of performance values and providingestimates of performance gains. The final part of this chapter discusses the specific dataset characteristics used in different machine learning tasks, including classification, regression, time series, and clustering.
Authors 4
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Affiliation as printed
Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
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Affiliation as printed
Porto Business School, Porto, Portugal
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Eindhoven University of Technology
Affiliation as printed
Department of Mathematics and Computer Science, Technische Universiteit Eindhoven, Eindhoven, The Netherlands
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