This repository contains the online supplement of the 2020 AAAI paper Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection. It contains: the appendix, the code for the experiments, and the benchmark data.
The paper authors are from the DTAI group of the KU Leuven:
Anomaly detection attempts to identify instances that deviate from expected behavior. Constructing performant anomaly detectors on real-world problems often requires some labeled data, which can be difficult and costly to obtain. However, often one considers multiple, related anomaly detection tasks. Therefore, it may be possible to transfer labeled instances from a related anomaly detection task to the problem at hand. This paper proposes a novel transfer learning algorithm for anomaly detection that selects and transfers relevant labeled instances from a source anomaly detection task to a target one. Then, it classifies target instances using a novel semi-supervised nearest-neighbors technique that considers both unlabeled target and transferred, labeled source instances. The algorithm outperforms a multitude of state-of-the-art transfer learning methods and unsupervised anomaly detection methods on a large benchmark. Furthermore, it outperforms its rivals on a real-world task of detecting anomalous water usage in retail stores.
In short, the paper tackles the following task:
GIVEN: a (partially) labeled source dataset Ds and
an unlabeled target dataset Dt from the same feature space;
DO: assign an anomaly score to each instance in Dt
using both Dt and a subset of Ds.The appendix to the paper (and the full conference paper) can either be accessed in LocIT/paper/ or via the webpage.
The Python code for the full LocIT algorithm and some of the baselines used in the paper, is in the folder: LocIT/models/
The zipped benchmark datasets and the script to construct them are in the folder: LocIT/data/
The core contributions of our paper are the LocIT transfer learning algorithm and the SSkNNO semi-supervised anomaly detection algorithm. Although the source code of these algorithms is also available in this repository for completeness, the recommended way to use these algorithms in your own work is to import them from the publicly available and pip-installable transfertools and anomatools packages.
pip install transfertools
pip install anomatoolsOnce installed, the models can be used as follows:
from transfertools.models import LocIT
from anomatools.models import SSkNNOImplementations of the remaining baselines used in the paper can be found here:
| algorithm | link |
|---|---|
| CBIT | transfertools |
| TCA | transfertools |
| GFK | link |
| JDA | link |
| TJM | link |
| JGSA | link |
| LOF | scikit-learn |
| HBOS | HBOS-python |
Feel free to ask questions: vincent.vercruyssen@kuleuven.be
@inproceedings{vercruyssen2020transfer,
title = {Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection},
author = {Vercruyssen, Vincent and Meert, Wannes and Davis Jesse},
booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence},
year = {2020}
}