Data Mining Methods for Optimizing Feature Extraction and Model Selection

Conference proceedings article

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Strategic Research Themes

Publication Details

Author list: Rouzbahman, Masha; Jovicic, Alexandra; Wang, Lu; Zucherman, Leon; Abul-Basher, Zahid; Charoenkitkarn, Nipon; Chignell, Mark;

Publisher: Hindawi

Publication year: 2020

Start page: 1

End page: 8

Number of pages: 8

ISBN: 9781450377591

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089183156&doi=10.1145%2f3406601.3406602&partnerID=40&md5=6b5e7f34e44160c672470bb79138b39e

Languages: English-Great Britain (EN-GB)

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Abstract

How can we carry out on-the-fly data mining on massive amounts of data, to make relevant predictions, based on data for similar observations to the one currently under consideration? In this paper we show the benefit of using large numbers of computationally efficient analyses to tune the feature extraction, and prediction, steps in data mining, using cross-validated prediction accuracy as the evaluative criterion. Different feature extraction strategies are also compared in terms of their predictive effectiveness in this context. While the research reported here focused on clinical prediction of healthcare outcomes, the results should have broader implications for large scale data mining in general. © 2020 ACM.

Keywords

Information Engineering