Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 561189
Title Real-time adaptive residual calculation for detecting trend deviations in systems with natural variability
Author(s) Woudenberg, Steven P.D.; Gaag, Linda C. van der; Feelders, Ad; Elbers, Armin R.W.
Source In: Advances in Intelligent DataAnalysis XIII - 13th International Symposium, IDA 2014, Proceedings. - Springer Verlag (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ) - ISBN 9783319125701 - p. 380 - 392.
Event PAKDD 2006 International Workshop on Knowledge Discovery in Life Science Literature, KDLL 2006, Singapore, 2006-04-09/2006-04-09
Department(s) Diagnostics & Crisis Organization
Publication type Contribution in proceedings
Publication year 2014
Abstract

Real-time detection of potential problems from animal production data is challenging, since these data do not just include chance fluctuations but reflect natural variability as well. This variability makes future observations from a specific instance of the production process hard to predict, even though a general trend may be known. Given the importance of well-established residuals for reliable detection of trend deviations, we present a new method for real-time residual calculation which aims at reducing the effects of natural variability and hence results in residuals reflecting chance fluctuations mostly. The basic idea is to exploit prior knowledge about the general expected data trend and to adapt this trend to the instance of the production process at hand as real data becomes available. We study the behavioural performance of our method by means of artificially generated and real-world data, and compare it against Bayesian linear regression.

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