Open AccessPredictive Business Process Monitoring with Tree-based Classification Algorithms
Author(s) -
Tomasz Owczarek,
Piotr Janke
Publication year - 2018
Publication title -
logistyka i transport
Language(s) - English
Resource type - Journals
ISSN - 1734-2015
DOI - 10.26411/83-1734-2015-4-40-10-18
Subject(s) - boosting (machine learning) , computer science , random forest , algorithm , event (particle physics) , decision tree , process (computing) , gradient boosting , machine learning , business process , artificial intelligence , data mining , tree (set theory) , decision tree learning , work in process , engineering , mathematics , mathematical analysis , operations management , physics , quantum mechanics , operating system
Predictive business process monitoring is a current research area which purpose is to predict the outcome of a whole process (or an element of a process i.e. a single event or task) based on available data. In the article we explore the possibility of use of the machine learning classification algorithms based on trees (CART, C5.0, random forest and extreme gradient boosting) in order to anticipate the result of a process. We test the application of these algorithms on real world event-log data and compare it with the known approaches. Our results show that.