2017, Tom 25 Nr 2

Stały URI dla kolekcjihttp://hdl.handle.net/11652/3827

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20171

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Autorsearch.filters.applied.operator.equals: search.filters.author.Duraj, Agnieszka

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  • Pozycja
    Outlier Detection Using the Multiobjective Genetic Algorithm
    (Wydawnictwo Politechniki Łódzkiej, 2017) Duraj, Agnieszka; Chomątek, Łukasz
    Since almost all datasets may be affected by the presence of anomalies which may skew the interpretation of data, outlier detection has become a crucial element of many datamining applications. Despite the fact that several methods of outlier detection have been proposed in the literature, there is still a need to look for new, more effective ones. This paper presents a new approach to outlier identification based on genetic algorithms. The study evaluates the performance and examines the features of several multiobjective genetic algorithms.