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@article{
 title = {Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting},
 type = {article},
 year = {2013},
 identifiers = {[object Object]},
 keywords = {Bayesian networks,Discretization,Environmental modelling,Feature subset selection,Missing imputation,Multi-dimensional classification,Recruitment forecasting,Supervised classification},
 pages = {245-254},
 volume = {40},
 websites = {http://www.sciencedirect.com/science/article/pii/S1364815212002472},
 month = {2},
 id = {26edd41d-4cf1-369a-876f-85ee33da467a},
 created = {2015-04-11T19:52:27.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {71a29c65-85d2-3809-a3a1-fe4a94dc78d2},
 last_modified = {2017-03-14T14:27:45.955Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {A multi-species approach to fisheries management requires taking into account the interactions between species in order to improve recruitment forecasting of the fish species. Recent advances in Bayesian networks direct the learning of models with several interrelated variables to be forecasted simultaneously. These models are known as multi-dimensional Bayesian network classifiers (MDBNs). Pre-processing steps are critical for the posterior learning of the model in these kinds of domains. Therefore, in the present study, a set of ‘state-of-the-art’ uni-dimensional pre-processing methods, within the categories of missing data imputation, feature discretization and feature subset selection, are adapted to be used with MDBNs. A framework that includes the proposed multi-dimensional supervised pre-processing methods, coupled with a MDBN classifier, is tested with synthetic datasets and the real domain of fish recruitment forecasting. The correctly forecasting of three fish species (anchovy, sardine and hake) simultaneously is doubled (from 17.3% to 29.5%) using the multi-dimensional approach in comparison to mono-species models. The probability assessments also show high improvement reducing the average error (estimated by means of Brier score) from 0.35 to 0.27. Finally, these differences are superior to the forecasting of species by pairs.},
 bibtype = {article},
 author = {Fernandes, Jose A. and Lozano, Jose A. and Inza, Iñaki and Irigoien, Xabier and Pérez, Aritz and Rodríguez, Juan D.},
 journal = {Environmental Modelling & Software}
}