Towards cost-sensitive adaptation: when is it worth updating your predictive model?

Zliobaite, I., Budka, M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2015) Towards cost-sensitive adaptation: when is it worth updating your predictive model? Neurocomputing, 150 (A). pp. 240-249. ISSN 0925-2312 doi: 10.1016/j.neucom.2014.05.084

Abstract/Summary

Our digital universe is rapidly expanding,more and more daily activities are digitally recorded, data arrives in streams, it needs to be analyzed in real time and may evolve over time. In the last decade many adaptive learning algorithms and prediction systems, which can automatically update themselves with the new incoming data, have been developed. The majority of those algorithms focus on improving the predictive performance and assume that model update is always desired as soon as possible and as frequently as possible. In this study we consider potential model update as an investment decision, which, as in the financial markets, should be taken only if a certain return on investment is expected. We introduce and motivate a new research problem for data streams ? cost-sensitive adaptation. We propose a reference framework for analyzing adaptation strategies in terms of costs and benefits. Our framework allows to characterize and decompose the costs of model updates, and to asses and interpret the gains in performance due to model adaptation for a given learning algorithm on a given prediction task. Our proof-of-concept experiment demonstrates how the framework can aid in analyzing and managing adaptation decisions in the chemical industry.

Additional Information	Special edition: Bioinspired and knowledge based techniques and applications — Selected papers from the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2012) The Vitality of Pattern Recognition and Image Analysis — Selected papers from the 6th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2013)
Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/38834
Identification Number/DOI	10.1016/j.neucom.2014.05.084
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	evolving data streams, concept drift, evaluation, cost-sensitive adaptation, utility of data mining
Additional Information	Special edition: Bioinspired and knowledge based techniques and applications — Selected papers from the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2012) The Vitality of Pattern Recognition and Image Analysis — Selected papers from the 6th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2013)
Publisher	Elsevier
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