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dc.contributor.author Diao, Ren
dc.contributor.author Shen, Qiang
dc.date.accessioned 2011-09-06T10:32:24Z
dc.date.available 2011-09-06T10:32:24Z
dc.date.issued 2011-09-06
dc.identifier.citation Diao , R & Shen , Q 2011 , ' Fuzzy-rough Classifier Ensemble Selection ' pp. 1516-1522 . en
dc.identifier.other PURE: 178422
dc.identifier.other dspace: 2160/7549
dc.identifier.uri http://hdl.handle.net/2160/7549
dc.description Diao R., Shen, Q. (2011). Fuzzy-rough Classifier Ensemble Selection. Proceedings of the 20th International Conference on Fuzzy Systems(Fuzz-IEEE 2011), pp. 1516-1522. en
dc.description.abstract Classifier ensembles constitute one of the main research directions in machine learning and data mining. Ensembles allow higher accuracy to be achieved which is otherwise often not achievable with a single classifier. A number of approaches have been adopted for constructing classifier ensembles and aggregate ensemble decisions. In most cases, these constructed ensembles contain redundant members that, if removed, may further increase ensemble diversity and produce better results. Smaller ensembles also relax the memory and storage requirements of an ensemble system, reducing its runtime overhead while improving overall efficiency. In this paper, a new approach to classifier ensemble selection based on fuzzyrough feature selection and harmony search is proposed. By transforming the ensemble predictions into training samples, classifiers are treated as features. Harmony search is then used to select a minimal subset of such artificial features that maximises the fuzzy-rough dependency measure. The resulting technique is compared against the original ensemble and ensembles formed using random selection, under both single algorithm and mixed classifier ensemble environments. en
dc.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Fuzzy-rough Classifier Ensemble Selection en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en


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