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Statistical learning procedures for monitoring regulatory compliance: an application to fisheries data

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As a special case of statistical learning, ensemble methods are well suited for the analysis of opportunistically collected data that involve many weak and sometimes specialized predictors, especially when subject-matter knowledge favours inductive approaches. We analyse data on the incidental mortality of dolphins in the purse-seine fishery for tuna in the eastern Pacific Ocean. The goal is to identify those rare purse-seine sets for which incidental mortality would be expected but none was reported. The ensemble method random forests is used to classify sets according to whether mortality was (response 1) or was not (response 0) reported. To identify questionable reporting practice, we construct ‘residuals’ as the difference between the categorical response (0,1) and the proportion of trees in the forest that classify a given set as having mortality. Two uses of these residuals to identify suspicious data are illustrated. This approach shows promise as a means of identifying suspect data gathered for environmental monitoring.

Keywords: Data quality; Ensemble; Environmental monitoring; Fisheries; Random forest

Document Type: Research Article


Affiliations: 1: Inter-American Tropical Tuna Commission, La Jolla, USA 2: University of Pennsylvania, Philadelphia, USA

Publication date: July 1, 2007

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