Supervised vs. Unsupervised Learning for Intentional Process Model Discovery - Université Paris 1 Panthéon-Sorbonne Access content directly
Conference Papers Year : 2014

Supervised vs. Unsupervised Learning for Intentional Process Model Discovery

Abstract

Learning humans' behavior from activity logs requires choos- ing an adequate machine learning technique regarding the situation at hand. This choice impacts signi cantly results reliability. In this paper, Hidden Markov Models (HMMs) are used to build intentional process models (Maps) from activity logs. Since HMMs parameters require to be learned, the main contribution of this paper is to compare supervised and unsupervised learning approaches of HMMs. After a theoretical compari- son of both approaches, they are applied on two controlled experiments to compare the Maps thereby obtained. The results demonstrate using su- pervised learning leads to a poor performance because it imposes binding conditions in terms of data labeling, introduces inherent humans' biases, provides unreliable results in the absence of ground truth, etc. Instead, unsupervised learning obtains e cient Maps with a higher performance and lower humans' effort.
Fichier principal
Vignette du fichier
bpmds2014_submission_25.pdf (341.64 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00994165 , version 1 (21-05-2014)

Identifiers

Cite

Ghazaleh Khodabandelou, Charlotte Hug, Rebecca Deneckere, Camille Salinesi. Supervised vs. Unsupervised Learning for Intentional Process Model Discovery. Business Process Modeling, Development, and Support (BPMDS), Jun 2014, Thessalonique, Greece. pp.1-15, ⟨10.1007/978-3-662-43745-2_15⟩. ⟨hal-00994165⟩

Collections

UNIV-PARIS1 CRI
461 View
1554 Download

Altmetric

Share

Gmail Facebook X LinkedIn More