Outlier Detection Using the Multiobjective Genetic Algorithm

Abstract

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.