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Auflistung nach Autor:in "Ivanovska, Aneta"

1 - 2 von 2
  • Konferenzbeitrag
    Studying the Presence of Genetically Modified Variants in Organic Oilseed Rape by Using Relational Data Mining
    (Environmental Informatics and Systems Research, 2007) Ivanovska, Aneta; Vens, Celine; Dzeroski, Saso; Colbach, Nathalie
    The production of genetically-modified (GM) crops has increased rapidly over the last 10 years. The possibility of GM crops mixing with conventional or organic crops is becoming a problem and estimating the adventitious presence of GM seeds into conventional crop harvests presents a challenge. In this study we used outputs from a previously developed computer model for gene flow between GM and conventional oilseed rape to construct relational classification trees that predict the adventitious presence of GM seeds in the central field of a large-risk field pattern as a function of cultivation practices. Unlike propositional data mining methods, relational methods (relational classification trees) enable us to examine the relations among fields, for example, the influence of the neighbouring fields on the adventitious presence of GM seeds in a given field. For that purpose we used the relational data mining system TILDE.
  • Konferenzbeitrag
    Using Simulation Models and Data Mining to Study Co-Existence of GM/Non-GM Crops at Regional Level
    (Managing Environmental Knowledge, 2006) Ivanovska, Aneta; Panov, Pance; Colbach, Nathalie; Debeljak, Marko; Dzeroski, Saso; Messean, Antoine
    Genetically-modified (GM) crops increased their share in EU agriculture, so the adventitious presence of GM varieties in non-GM seeds and crops has become an issue and poses the problem of their co-existence with conventional and organic crops. Therefore, there is a need to propose appropriate measures at the farm and regional levels to minimize adventitious presence of GM crops. Outputs from the previously developed GENESYS model for gene flow between cropped and volunteer oilseed rape were used to make rule-based models that predict the rates of adventitious presence of GM seeds in the central field of a large-risk field pattern. Data aggregation was carried out to investigate if the regional variables improve the prediction quality of the rule-based model.