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Mobile Data MiningFeature Engineering

Mobile Data Mining: Feature Engineering [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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Mobile Data MiningFeature Engineering

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Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
ISBN
978-3-030-02100-9
Pages
17 –23
DOI
10.1007/978-3-030-02101-6_3
Publisher site
See Chapter on Publisher Site

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.]

Published: Nov 1, 2018

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