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Feature Engineering Based on Sensory Data

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Machine Learning for the Quantified Self

Part of the book series: Cognitive Systems Monographs ((COSMOS,volume 35))

Abstract

Approaches to automatically generate useful features from sensory data are introduced in this chapter. Most of the approaches introduced focus on datasets that have a temporal ordering. Features in the time domain are explained, thereby summarizing both numerical and categorical values in a certain historical window. The frequency domain is also discussed, including Fourier transformations and features one can derive from these transformations. In addition, the extraction of features from unstructured data is discussed, mainly focusing on text data.

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Correspondence to Mark Hoogendoorn .

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Hoogendoorn, M., Funk, B. (2018). Feature Engineering Based on Sensory Data. In: Machine Learning for the Quantified Self. Cognitive Systems Monographs, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-66308-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-66308-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66307-4

  • Online ISBN: 978-3-319-66308-1

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