https://github.com/kotlarmilos/meta-features-anomaly-detection

Set of meta-features for model selection in anomaly detection tasks based on domain-specific properties
https://github.com/kotlarmilos/meta-features-anomaly-detection

anomaly-detection automl machine-learning meta-features

Last synced: 27 days ago
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Set of meta-features for model selection in anomaly detection tasks based on domain-specific properties

• Host: GitHub
• URL: https://github.com/kotlarmilos/meta-features-anomaly-detection
• Owner: kotlarmilos
• Created: 2020-06-04T19:19:31.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-01-31T21:48:09.000Z (about 3 years ago)
• Last Synced: 2024-11-09T21:08:56.950Z (3 months ago)
• Topics: anomaly-detection, automl, machine-learning, meta-features
• Language: Jupyter Notebook
• Homepage:
• Size: 15.3 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

This repository contains the scripts which evaluate a novel set of meta-features for model selection in anomaly detection tasks based on domain-specific properties. 

By using different kinds of metadata, such as the properties of the data, algorithm properties, or

correlation previously derived from the data, it is possible to select different models to effectively solve a

given anomaly detection task. The meta-learning approach based on a set of meta-features that describes

data properties and correlation can enable efficient model selection in AutoML frameworks.

Experiments with 63 datasets from different repositories with varying schemas show that

the proposed set of meta-features achieves the accuracy of 87% for model selection, while the achieved

accuracy for simple meta-features is 74%, for statistical meta-features 68%, for information theory metafeature

70%, and for a comprehensive set of meta-features by [pyMFE](https://pypi.org/project/pymfe/) 73%.

Results are in [/results](https://github.com/kotlarmilos/meta-features-anomaly-detection/tree/master/pycharm/results) directory. Evaluated algorithms are in [/algorithms](https://github.com/kotlarmilos/meta-features-anomaly-detection/tree/master/pycharm/algorithms) directory.

## Datasets

Datasets are collected from repositories in [Harvard Dataverse](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF) and [Numenta Anomaly Benchmark](https://github.com/numenta/NAB) and cover a broad range of domains including manufacturing, transportation, healthcare, intrusion detection, and system log analysis.

Dataset repository is available [here](https://drive.google.com/drive/folders/1hp9ZuVQRnRduzKzMMnyNQ52Gv9QqQATP?usp=sharing) and [here](https://drive.google.com/drive/folders/10j-x2yJsBOSsjkeX1S8FtkaJqfrkkJ7-?usp=sharing).

## Prerequests

 - Python 3.7

 - Pip

 - Numpy