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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Multiclass Outlier Detection and Visualization Based on Isolation Forest |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2022 |
| Abstract Text | Isolation forest is a popular anomaly detector due to its model-free structure and validity in detecting outliers across different types of datasets. This thesis presents solutions for two main issues in terms of isolation-based outlier detection algorithms. First, there is an adjustment to the isolation-based outlier detection model to improve the detection accuracy of Isolation Forest (iForest) and Extended Isolation Forest (EIF). The EIF is an extension of iForest, which addresses the block artifacts issue of iForest. Motivated by the failures of outlier detection on some real-world benchmark datasets by EIF, an adjusted EIF regarding the problem arising from the randomness of split hyperplane is presented. The outlier detection accuracy and precision of the adjusted EIF show that it is capable of enhancing the performance of both iForest and EIF. However, the drawback of the adjustment is that it is not time efficient. Second, this thesis proposes methods to generate a credible image presentation with outliers scattered in the relative areas based on isolation-based detection for multi-variate datasets. Inspired by the fact that neither iForest nor EIF can detect local outliers for each class in multi-class datasets, and few related works have been done in this direction, several class-wise detectors based on EIF and adjusted EIF are proposed in this thesis. By comparing the graphs, one of the methods achieves the best performance in providing insights for identifying potential outliers in clustering datasets. |
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