Machine learning based work task classification

Michael Granitzer (1, 2) , Andreas S. Rath (1) , Mark Kröll (2) , Christin Seifert (1) , Doris Ipsmiller (3) , Didier Devaurs (1) , Nicolas Weber (1, 2) , Stefanie Lindstaedt (1, 2)

1 Know-Center (AUSTRIA)
2 Knowledge Management Institute (Graz, Austria)
3 m2n - consulting and development gmbh (Linz, Australie)

Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a user's current work task, i.e. the current work context a user resides in. In this work we present and evaluate machine learning based work task detection methods. By viewing a work task as sequence of digital interaction patterns of mouse clicks and key strokes, we present (i) a methodology for recording those user interactions and (ii) an in-depth analysis of supervised classification models for classifying work tasks in two different scenarios: a task centric scenario and a user centric scenario. We analyze different supervised classification models, feature types and feature selection methods on a laboratory as well as a real world data set. Results show satisfiable accuracy and high user acceptance by using relatively simple types of features.

Mots clés: Task classification

Domaines: Apprentissage [cs.LG]

Michael Granitzer, Andreas S. Rath, Mark Kröll, Christin Seifert, Doris Ipsmiller, et al.. Machine learning based work task classification. Journal of Digital Information Management, 2009, 7 (5), pp.306-313. ⟨hal-00872101⟩