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

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

A growing number of research papers shed light on automated machine learning (AutoML) frameworks, which are becoming a promising solution for building complex machine learning models without human expertise and assistance. The key challenge in enabling AutoML frameworks to build an efficient model f...

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Bibliographic Details
Main Authors: Milos Kotlar, Marija Punt, Zaharije Radivojevic, Milos Cvetanovic, Veljko Milutinovic
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Anomaly detection
AutoML
data properties
distance functions
meta-features
meta-learning
Online Access:https://ieeexplore.ieee.org/document/9461173/

Internet

https://ieeexplore.ieee.org/document/9461173/

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