Publications in 2015

2015

BibSonomy

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

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

3D Regression Heat Map Analysis of Population Study Data. IEEE Transactions on Visualization and Computer Graphics (TVCG), (22)1:81-90, 2015.

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

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

Challenges in Mining Evolving Data Streams. 2015.

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

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.

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

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

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

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

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

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

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

Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI). Brain Informatics, 1-12, Springer Berlin Heidelberg, 2015. URL

Learning Relational User Profiles and Recommending Items as Their Preferences Change. International Journal on Artificial Intelligence Tools, (24)02:31 pages, 2015. URL

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

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.

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.

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

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