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

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Milos Kotlar ◽  
Marija Punt ◽  
Zaharije Radivojevic ◽  
Milos Cvetanovic ◽  
Veljko Milutinovic
Author(s):  
Roman Budjač ◽  
Marcel Nikmon ◽  
Peter Schreiber ◽  
Barbora Zahradníková ◽  
Dagmar Janáčová

Abstract This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.


Author(s):  
Zhumakhan Nazir ◽  
Dinmukhamed Kaldykhanov ◽  
Kozy-Korpesh Tolep ◽  
Jurn-Gyu Park

Author(s):  
Basheer Qolomany ◽  
Ihab Mohammed ◽  
Ala Al-Fuqaha ◽  
Mohsen Guizani ◽  
Junaid Qadir

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