Supervised outlier detection for classification and regression
EntidadUAM. Departamento de Ingeniería Informática
Fecha de edición2022-05-14
10.1016/j.neucom.2022.02.047Neurocomputing 486 (2022): 77-92
Financiado porThe authors acknowledge financial support from the European Regional Development Fund and the Spanish State Research Agency of the Ministry of Economy, Industry, and Competitiveness under the projects TIN2016-76406-P (AEI/FEDER, UE) and PID2019-106827GB-I00. 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
ProyectoGobierno de España. TIN2016-76406-P; Gobierno de España. PID2019-106827GB-I00
Versión del editorhttps://doi.org/10.1016/j.neucom.2022.02.047
MateriasClassification; Histogram based outlier detection; Isolation forests; Local outlier factor; Minimum covariance determinant; Optimal hyperparameter selection; Outlier detection; Regression; Supervised learning; Informática
Derechos© The author(s)
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Outlier detection, i.e., the task of detecting points that are markedly different from the data sample, is an important challenge in machine learning. When a model is built, these special points can skew the model training and result in less accurate predictions. Due to this fact, it is important to identify and remove them before building any supervised model and this is often the first step when dealing with a machine learning problem. Nowadays, there exists a very large number of outlier detector algorithms that provide good results, but their main drawbacks are their unsupervised nature together with the hyperparameters that must be properly set for obtaining good performance. In this work, a new supervised outlier estimator is proposed. This is done by pipelining an outlier detector with a following a supervised model, in such a way that the targets of the later supervise how all the hyperparameters involved in the outlier detector are optimally selected. This pipeline-based approach makes it very easy to combine different outlier detectors with different classifiers and regressors. In the experiments done, nine relevant outlier detectors have been combined with three regressors over eight regression problems as well as with two classifiers over another eight binary and multi-class classification problems. The usefulness of the proposal as an objective and automatic way to optimally determine detector hyperparameters has been proven and the effectiveness of the nine outlier detectors has also been analyzed and compared
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