This study investigates facets of shallow machine learning as an accurate data-centric approach to predict business process behaviour. Shallow machine learning is investigated as a part of a holistic approach that combines feature construction, local and global learning, classification and regression algorithms. Experiments show that, despite the emerging attention towards deep learning also in predictive process mining, stacking feature construction and shallow ma-chine learning algorithms can still outperform various process predictor competitors (included deep learning ones).

Leveraging shallow machine learning to predict business process behavior

Appice A.
;
Di Mauro N.;Malerba D.
2019

Abstract

This study investigates facets of shallow machine learning as an accurate data-centric approach to predict business process behaviour. Shallow machine learning is investigated as a part of a holistic approach that combines feature construction, local and global learning, classification and regression algorithms. Experiments show that, despite the emerging attention towards deep learning also in predictive process mining, stacking feature construction and shallow ma-chine learning algorithms can still outperform various process predictor competitors (included deep learning ones).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/265534
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 4
social impact