Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.
2009
Increasing the productivity of a knowledgeworker via intelligent applications requires the identification ofa user’s current work task, i.e. the current work context a userresides in. In this work we present and evaluate machine learningbased work task detection methods. By viewing a work taskas sequence of digital interaction patterns of mouse clicks andkey strokes, we present (i) a methodology for recording thoseuser interactions and (ii) an in-depth analysis of supervised classificationmodels for classifying work tasks in two different scenarios:a task centric scenario and a user centric scenario. Weanalyze different supervised classification models, feature typesand feature selection methods on a laboratory as well as a realworld data set. Results show satisfiable accuracy and high useracceptance by using relatively simple types of features.
Lex Elisabeth, Granitzer Michael, Juffinger A., Seifert C.
2009
Cross-Domain Classification: Trade-Off between Complexity and Accuracy
Proceedings of the 4th International Conference for Internet Technology and Secured Transactions (ICITST) 2009
Text classification is one of the core applications in data
mining due to the huge amount of not categorized digital
data available. Training a text classifier generates a model
that reflects the characteristics of the domain. However, if
no training data is available, labeled data from a related
but different domain might be exploited to perform crossdomain
classification. In our work, we aim to accurately
classify unlabeled blogs into commonly agreed newspaper
categories using labeled data from the news domain. The
labeled news and the unlabeled blog corpus are highly dynamic
and hourly growing with a topic drift, so a trade-off
between accuracy and performance is required. Our approach
is to apply a fast novel centroid-based algorithm, the
Class-Feature-Centroid Classifier (CFC), to perform efficient
cross-domain classification. Experiments showed that
this algorithm achieves a comparable accuracy than k-NN
and is slightly better than Support Vector Machines (SVM),
yet at linear time cost for training and classification. The
benefit of this approach is that the linear time complexity enables
us to efficiently generate an accurate classifier, reflecting
the topic drift, several times per day on a huge dataset.