Quality-optimized predictive analytics

Anagnostopoulos, C. (2016) Quality-optimized predictive analytics. Applied Intelligence, 45(4), pp. 1034-1046. (doi: 10.1007/s10489-016-0807-x)

119430.pdf - Accepted Version



On-line statistical and machine learning analytic tasks over large- scale contextual data streams coming from e.g., wireless sensor networks, Inter- net of Things environments, have gained high popularity nowadays due to their significance in knowledge extraction, regression and classification tasks, and, more generally, in making sense from large-scale streaming data. The quality of the received contextual information, however, impacts predictive analytics tasks especially when dealing with uncertain data, outliers data, and data con- taining missing values. Low quality of received contextual data significantly spoils the progressive inference and on-line statistical reasoning tasks, thus, bias is introduced in the induced knowledge, e.g., classification and decision making. To alleviate such situation, which is not so rare in real time contextual information processing systems, we propose a progressive time-optimized data quality-aware mechanism, which attempts to deliver contextual information of high quality to predictive analytics engines by progressively introducing a certain controlled delay. Such a mechanism progressively delivers high qual- ity data as much as possible, thus eliminating possible biases in knowledge extraction and predictive analysis tasks. We propose an analytical model for this mechanism and show the benefits stem from this approach through com- prehensive experimental evaluation and comparative assessment with quality- unaware methods over real sensory multivariate contextual data.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos
Authors: Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Applied Intelligence
ISSN (Online):1573-7497
Published Online:27 June 2016
Copyright Holders:Copyright © 2016 Springer Science+Business Media
First Published:First published in Applied Intelligence 45(4): 1034-1046
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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