2015

Georg Krempl. Challenges in Mining Evolving Data Streams. 2015.

Zaigham Faraz Siddiqui, Georg Krempl, Myra Spiliopoulou, Jose M. Peña, Nuria Paul, and Fernando Maestu. Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI). Brain Informatics, 1-12, Springer Berlin Heidelberg, 2015. URL

Georg Krempl. Temporal Density Extrapolation. In Ahlame Douzal-Chouakria, José A. Vilar, Pierre-Francois Marteau, Ann Maharaj, Andrés M. Alonso, Edoardo Otranto, and Maria-Irina Nicolae (Eds.), Proc. of the 1st Int. Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) co-located with ECML PKDD 2015, (1425)CEUR Workshop Proceedings, 2015. URL

Georg Krempl, David Bodnar, and Anita Hrubos. When Learning Indeed Changes the World: Diagnosing Prediction-Induced Drift. In Tijl De Bie, Elisa Fromont, and Matthijs van Leeuwen (Eds.), Advances in Intelligent Data Analysis XIV - 14th Int. Symposium, IDA 2015, St. Etienne, France, (9385):XXII--XXIII, Springer, 2015.

Uli Niemann, Tommy Hielscher, Myra Spiliopoulou, Henry Völzke, and Jens-Peter Kühn. Can we classify the participants of a longitudinal epidemiological study from their previous evolution?. Proc. of the 28th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS15), 121-126, IEEE, São Carlos and Ribeirão Preto, Brazil, June 2015. URL

Pawel Matuszyk, and Myra Spiliopoulou. Semi-supervised Learning for Stream Recommender Systems. In Nathalie Japkowicz, and Stan Matwin (Eds.), Discovery Science, (9356):131-145, Springer International Publishing, 2015. URL

Paul Klemm, Kai Lawonn, Sylvia Glaßer, Uli Niemann, Katrin Hegenscheid, Henry Völzke, and Bernhard Preim. 3D Regression Heat Map Analysis of Population Study Data. IEEE Transactions on Visualization and Computer Graphics (TVCG), (22)1:81-90, 2015.

Christian Beyer, Georg Krempl, and Vincent Lemaire. How to Select Information That Matters: A Comparative Study on Active Learning Strategies for Classification. Proc. of the 15th Int. Conf. on Knowledge Technologies and Data-Driven Business (i-KNOW 2015), ACM, 2015. URL

Georg Krempl, Daniel Kottke, and Vincent Lemaire. Optimised probabilistic active learning (OPAL) For Fast, Non-Myopic, Cost-Sensitive Active Classification. In João Gama, Indrė Žliobaitė, Alípio M. Jorge, and Concha Bielza (Eds.), Machine Learning, 1-28, Springer US, 2015. URL

Zaigham Faraz Siddiqui, Eleftherios Tiakas, Panagiotis Symeonidis, Myra Spiliopoulou, and Yannis Manolopoulos. Learning Relational User Profiles and Recommending Items as Their Preferences Change. International Journal on Artificial Intelligence Tools, (24)02:31 pages, 2015. URL

Daniel Kottke, Georg Krempl, and Myra Spiliopoulou. Probabilistic Active Learning in Datastreams. In Elisa Fromont, Tijl De Bie, and Matthijs van Leeuwen (Eds.), Advances in Intelligent Data Analysis XIV, (9385):145-157, Springer International Publishing, 2015. URL

Georg Krempl, Daniel Kottke, and Vincent Lemaire. Optimised probabilistic active learning (OPAL). In João Gama, Indrė Žliobaitė, Alípio M. Jorge, and Concha Bielza (Eds.), Machine Learning, 1-28, Springer US, 2015. URL

Max Zimmermann, Eirini Ntoutsi, and Myra Spiliopoulou. Discovering and Monitoring Product Features and the Opinions on them with OPINSTREAM. Neurocomput., (Volume 150)Part A:Pages 1-346, Elsevier Science Publishers B. V., Amsterdam, The Netherlands, The Netherlands, 2015.

Pawel Matuszyk, João Vinagre, Myra Spiliopoulou, Alípio Mário Jorge, and João Gama. Forgetting Methods for Incremental Matrix Factorization in Recommender Systems. Proceedings of the 30th Annual ACM Symposium on Applied Computing, 947--953, ACM, New York, NY, USA, 2015. URL

Max Zimmermann, Eirini Ntoutsi, and Myra Spiliopoulou. Incremental Active Opinion Learning Over a Stream of Opinionated Documents. WISDOM'15 (Workshop on Issues of Sentiment Discovery and Opinion Mining) 2015 at Knowledge Discovery and Data Mining, KDD'15 Workshops 2015, Sydney, Australia, August 10, 2015, 2015.

Sebastian Wagner, Max Zimmermann, Eirini Ntoutsi, and Myra Spiliopoulou. Ageing-Based Multinomial Naive Bayes Classifiers Over Opinionated Data Streams. In Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, and Alípio Jorge (Eds.), Machine Learning and Knowledge Discovery in Databases, (9284):401-416, Springer International Publishing, 2015. URL

Vera Hofer, and Georg Krempl. Predicting and Monitoring Changes in Scoring Data. In Jonathan Crook, David Edelman, David Hand, and Christophe Mues (Eds.), Credit Scoring and Credit Control XIV (CSCC XIV), XIVThe University of Edinburgh, 2015. URL

Georg Krempl, Tuan Cuong Ha, and Myra Spiliopoulou. Clustering-Based Optimised Probabilistic Active Learning (COPAL). In Nathalie Japkowicz, and Stan Matwin (Eds.), Proc. of the 18th Int. Conf. on Discovery Science (DS 2015), (9356):101--115, Springer, 2015. URL

Max Zimmermann, Eirini Ntoutsi, and Myra Spiliopoulou. Extracting opinionated (sub)features from a stream of product reviews using accumulated novelty and internal re-organization. Information Sciences, -, 2015. URL

Stefan Räbiger, and Myra Spiliopoulou. A framework for validating the merit of properties that predict the influence of a twitter user. Expert Systems with Applications, (42)5:2824-2834, 2015. URL

Daniel Kottke, Gino Gulamhussene, and Klaus Tönnies. Data-Driven Spine Detection for Multi-Sequence MRI. In Heinz Handels, Thomas Martin Deserno, Hans-Peter Meinzer, and Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin (BVM2015), 5-10, Springer Berlin Heidelberg, 2015. URL