Duraj, AgnieszkaChomątek, Łukasz2021-07-152021-07-152017Duraj, A., & Chomątek, Łukasz. (2017). Outlier Detection Using the Multiobjective Genetic Algorithm. Journal of Applied Computer Science, 25(2), 29-42. https://doi.org/10.34658/jacs.2017.2.29-421507-0360http://hdl.handle.net/11652/3877https://doi.org/10.34658/jacs.2017.2.29-42Since 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.enFair use conditionDla wszystkich w zakresie dozwolonego użytkuoutliers detectiongenetic algorithmwykrywanie wartości odstającychalgorytm genetycznyArtykułLUT LicenseLicencja PŁhttps://doi.org/10.34658/jacs.2017.2.29-4210.34658/jacs.2017.2.29-42