Abstract
The detection of anomalies, i.e. of those points found in a dataset but which do not seem to be generated by the underlying distribution, is crucial in machine learning. Their presence is likely to make model predictions not as accurate as we would like; thus, they should be identified before any model is built which, in turn, may require the optimal selection of the detector hyperparameters. However, the unsupervised nature of this problem makes that task not easy. In this work, we propose a new estimator composed by an anomaly detector followed by a supervised model; we can take then advantage of this second model to transform model estimation into a supervised problem and, as a consequence, the estimation of the detector hyperparameters can be done in a supervised setting. We shall apply these ideas to optimally hyperparametrize four different anomaly detectors, namely, Robust Covariance, Local Outlier Factor, Isolation Forests and One-class Support Vector Machines, over different classification and regression problems. We will also experimentally show the usefulness of our proposal to estimate in an objective and automatic way the best detector hyperparameters.
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Acknowledgments
The authors acknowledge financial support from the European Regional Development Fund and from the Spanish Ministry of Economy, Industry, and Competitiveness - State Research Agency, project TIN2016-76406-P (AEI/FEDER, UE). They also thank the UAM–ADIC Chair for Data Science and Machine Learning and gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM.
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Bella, J., Fernández, Á., Dorronsoro, J.R. (2020). Supervised Hyperparameter Estimation for Anomaly Detection. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_20
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DOI: https://doi.org/10.1007/978-3-030-61705-9_20
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