Classification of Oil Spills using Computational Intelligence

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dc.categoryJournal Article
dc.contributor.authorPadmavathi, G
dc.date.accessioned2017-03-28T22:33:11Z
dc.date.available2017-03-28T22:33:11Z
dc.date.issued2013
dc.departmentComputer Scienceen_US
dc.description.abstractRough set theory is one of the useful cohiputati^ni^Ulhfelligence tools which can be used to deal with vague information and to take important decision in uheeftain domain. Rough set theory is useful for decision making in situation where indiscernibility is present. Rough sets do not require experienced knowledge engineers to provide dditional information about the niembership functigns for being processed. In this paper Rough Set method is used ar dimensionality reduction of oil spill feature space in SAR imagery. Reducing the number of features results in reduced feature space which improves the prediction, accuracy and minimizes the classification time.en_US
dc.identifier.urihttps://ir.avinuty.ac.in/handle/avu/2244
dc.langEnglishen_US
dc.publisher.nameInternational Journal of Advanced Networking And Applicationen_US
dc.publisher.typeInternationalen_US
dc.titleClassification of Oil Spills using Computational Intelligenceen_US
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