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dc.contributor.authorBlockeel, Hendrik
dc.contributor.authorDžeroski, Sašo
dc.contributor.authorKompare, Boris
dc.contributor.authorKramer, Stefan
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorVan Laer, Wim
dc.date.accessioned2008-11-25T03:54:43Z
dc.date.available2008-11-25T03:54:43Z
dc.date.issued2004
dc.identifier.citationBlockeel H., Dzeroski S., Kompare B., Kramer S., Pfahringer B. & Van Laer W.(2004). Experiments in Predicting Biodegradability. Applied Artificial Intelligence, 18(2), 157-181.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1462
dc.description.abstractThis paper is concerned with the use of AI techniques in ecology. More specifically, we present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). The activity we want to predict is the biodegradability of chemical compounds in water. In particular, the target variable is the half-life for aerobic aqueous biodegradation. Structural descriptions of chemicals in terms of atoms and bonds are derived from the chemicals' SMILES encodings. The definition of substructures is used as background knowledge. Predicting biodegradability is essentially a regression problem, but we also consider a discretized version of the target variable. We thus employ a number of relational classification and regression methods on the relational representation and compare these to propositional methods applied to different propositionalizations of the problem. We also experiment with a prediction technique that consists of merging upper and lower bound predictions into one prediction. Some conclusions are drawn concerning the applicability of machine learning systems and the merging technique in this domain and the evaluation of hypotheses.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherTaylor & Francisen_US
dc.relation.urihttp://www.informaworld.com/smpp/content~content=a713768160~db=all~order=pageen_US
dc.rightsThis is an author’s version of an article published in journal: Applied Artificial Intelligence. Copyright © 2008 Taylor & Francis.en_US
dc.subjectcomputer scienceen_US
dc.subjectartificial intelligenceen_US
dc.subjectMachine learning
dc.titleExperiments in Predicting Biodegradabilityen_US
dc.typeConference Contributionen_US
dc.identifier.doi10.1080/08839510490279131en_US
pubs.declined2014-06-05T17:47:32.470+1200
pubs.deleted2014-06-05T17:47:32.470+1200
pubs.elements-id30861


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