Abstract

So far, the collected data are time series data of different sensors’ readings. To make use of these time series in the following learning models, we usually need to first slice the time series data into data segments, and then extract features from these segments. Meanwhile, the data segmentation and feature extraction also affect the aspects like energy efficiency, model accuracy, and response time. In this chapter, we first discuss the data segmentation method, and then introduce the feature extraction which extracts features from a segment with the principle that the extracted features should be informative and discriminative. To further save time, we also discuss the feature selection method which selects a subset of the features for the current task.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages17-23
Number of pages7
DOIs
StatePublished - Jan 1 2018

Publication series

NameSpringerBriefs in Computer Science
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Yao, Y., Su, X., & Tong, H. (2018). Feature Engineering. In SpringerBriefs in Computer Science (pp. 17-23). (SpringerBriefs in Computer Science). Springer. https://doi.org/10.1007/978-3-030-02101-6_3