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Machine learning based work task classification

Granitzer, Michael and Rath, Andreas S. and Kröll, Mark and Seifert, Cristin and Ipsmiller, Doris and Devaurs, Didier and Weber, Nicolas and Lindstaedt, Stefanie N. Machine learning based work task classification. (2009) Journal of Digital Information Management, 7 (5). 306-313. ISSN 0972-7272

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Abstract

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.

Item Type:Article
Additional Information:Thanks to Digital Information Research Foundation. The original publication is available at http://www.dirf.org/jdim/
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Other partners > Graz University of Technology - TU Graz (AUSTRIA)
Other partners > Know Center (AUSTRIA)
Other partners > M2n Intelligent Management (AUSTRIA)
Statistics:download
Deposited By: Didier Devaurs
Deposited On:13 Mar 2013 10:55

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