Krempl, Georg
2021
Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372). In Georg Krempl, Vera Hofer, Geoffrey Webb, and Eyke Hüllermeier (Eds.), Dagstuhl Reports, (10)4:1--36, Schloss Dagstuhl -- Leibniz-Zentrum für Informatik, Dagstuhl, Germany, 2021. URL
2020
Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder. In Michael R. Berthold, Ad Feelders, and Georg Krempl (Eds.), Advances in Intelligent Data Analysis XVIII, 1--13, Springer International Publishing, Cham, 2020. URL
2019
Temporal Density Extrapolation using a Dynamic Basis Approach.. In K. Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen (Eds.), Data Mining and Knowledge Discovery, (Special Issue of the ECML/PKDD 2019 Journal Track)2019. URL
2016
Active Subtopic Detection in Multitopic Data. In Georg Krempl, Vincent Lemaire, Edwin Lughofer, and Daniel Kottke (Eds.), Proc. of the IKNOW-Workshop on Active Learning: Applications, Foundations and Emerging Trends, CEUR Workshop Proceedings, 2016. URL
Active Selection of Difficult Classes. Tagung der Deutschen Arbeitsgemeinschaft Statistik (DAGSTAT), 2016. URL
Tutorial on Active Learning: Applications, Foundations and Emerging Trends. 2016.
Learning from monitoring unlabelled data under large verification latency. In Iris Pigeot, and Eyke Hüllermeier (Eds.), Joint Statistical Meeting DAGStat2016, Big Data and Data Science Track, 2016.
Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. In Georg Krempl, Vincent Lemaire, Edwin Lughofer, and Daniel Kottke (Eds.), Active Learning: Applications, Foundations and Emerging Trends @iKnow 2016, 25-34, 2016. URL
Workshop on Active Learning: Applications, Foundations and Emerging Trends. In Georg Krempl, Vincent Lemaire, Edwin Lughofer, and Daniel Kottke (Eds.), CEUR Workshop Proceedings, (1707)Aachen University, Aachen, 2016. URL
Kombiniert Lernen - Effizient und effektiv Lehren. In Philipp Pohlenz (Eds.), Best practice in teaching, Tag der Lehre 2016, Magdeburg University., Jun 22, 2016. URL
Profilierung interdisziplinärer Nachwuchswissenschaftler -- Profilstudium und Summerschool Lernende Systeme / Biocomputing. In Barbara Paech (Eds.), Best practices in teaching, Deutscher Fakultätentag Informatik, 2016. URL
Multi-Class Probabilistic Active Learning. In Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, and Frank van Harmelen (Eds.), ECAI, (285):586-594, IOS Press, 2016. URL
Temporal Aspects of Stream Active Learning. Tagung der Deutschen Arbeitsgemeinschaft Statistik (DAGSTAT), 2016. URL
Inferring Delayed Neural Network Connections. In John Aston, Claudia Kirch, and Hernando Ombao (Eds.), Conference on Novel Statistical Methods in Neuroscience (NeuroStat 2016), 2016.
Prediction-Induced Drift as New Form of Drift. In Iris Pigeot, and Eyke Hüllermeier (Eds.), Joint Statistical Meeting DAGStat2016, Big Data and Data Science Track, 2016.
Probabilistic Active Learning for Active Class Selection. In Kory Mathewson, Kaushik Subramanian, and Robert Loftin (Eds.), Proc. of the NIPS Workshop on the Future of Interactive Learning Machines, 2016. URL
On Temporal Density Extrapolation Using Kernels. In Miłosz Kadziński (Eds.), 28th European Conference on Operational Research (EURO 2016), 2016. URL
2015
Challenges in Mining Evolving Data Streams. 2015.
Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI). Brain Informatics, 1-12, Springer Berlin Heidelberg, 2015. URL
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.
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
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
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
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
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
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
2014
Are Some Brain Injury Patients Improving More Than Others?. The 2014 International Conference on Brain Informatics and Health (BIH \'14), Warsaw, Poland., 2014.
Tagungsband der Magdeburger-Informatik-Tage, 3. Doktorandentagung 2014 (MIT 2014). In Christian Hansen, Stefan Knoll, Veit Köppen, Georg Krempl, Claudia Krull, and Eike Schallehn (Eds.), Magdeburg University, 2014. URL
Probabilistic Active Learning: A Short Proposition. In Torsten Schaub, Gerhard Friedrich, and Barry O'Sullivan (Eds.), Proceedings of the 21st European Conference on Artificial Intelligence (ECAI2014), August 18 -- 22, 2014, Prague, Czech Republic, (263)IOS Press, 2014. URL
Open Challenges for Data Stream Mining Research. In Charu Aggarwal, Haixun Wang, Hanghang Tong, and Ankur M. Teredesai (Eds.), SIGKDD Explorations, (16 Special Issue on Big Data)1:1--10, 2014. URL
Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency. In Saso Dzeroski, Pance Panov, Dragi Kocev, and Ljupco Todorovski (Eds.), Proceedings of the 17th Int. Conf. on Discovery Science (DS), Bled, Springer, 2014. URL
2013
Real-World Challenges for Data Stream Mining - proceedings of the 1st International Workshop on Real-World Challenges for Data Stream Mining, RealStream 2013, Prague, Czech Republic, September 27, 2013. In Georg Krempl, Indre Zliobaite, Yin Wang, and Georg Forman (Eds.), (Online)Magdeburg University, 2013. URL
Correcting the Usage of the Hoeffding Inequality in Stream Mining. In Allan Tucker, Frank Höppner, Arno Siebes, and Stephen Swift (Eds.), Advances in Intelligent Data Analysis XII, (8207):298-309, Springer Berlin Heidelberg, 2013. URL
Drift mining in data: A framework for addressing drift in classification. Computational Statistics and Data Analysis, (57)1:377-391, 2013.
Tagungsband der Magdeburger-Informatik-Tage, 2. Doktorandentagung 2013 (MIT 2013). In Robert Buchholz, Georg Krempl, Claudia Krull, Eike Schallehn, Sebastian Stober, Frank Ortmeier, and Sebastian Zug (Eds.), Magdeburg University, 2013. URL
Mining Multiple Threads of Streaming Data. Tutorial at the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Gold Coast, Australia, April 2013. URL
2012
Advanced Topics on Data Stream Mining: Part II. Mining Multiple Streams. Bristol, UK, 24-28 09 2012.
A hierarchical tree layout algorithm with an application to corporate management in a change process. Expert Systems with Applications, (39)15:12123-12130, 2012.
Tagungsband der 1. Doktorandentagung Magdeburger-Informatik-Tage (MIT 2012). In Georg Krempl, Claudia Krull, Frank Ortmeier, Eike Schallehn, and Sebastian Zug (Eds.), 2012. URL
2011
Adaptive Prediction Models and their Application to Credit Scoring. 2011.
Classification in Presence of Drift and Latency. In Myra Spiliopoulou, Haixun Wang, Diane Cook, Jian Pei, Wei Wang, Osmar Zaïane, and Xindong Wu (Eds.), Proceedings of the 11th IEEE International Conference on Data Mining Workshops (ICDMW 2011), IEEE, 2011.
The Algorithm APT to Classify in Concurrence of Latency and Drift. In João Gama, Elizabeth Bradley, and Jaakko Hollmén (Eds.), Advances in Intelligent Data Analysis X, (7014):222-233, Springer, 2011.
Drift Models and Classification in Presence of Latency and Drift. Proceedings of the Symposium Learning, Knowledge, Adaptation (LWA 2011) of the GI Special Interest Groups KDML, IR and WM., 65--72, September 2011.
Online Clustering of High-Dimensional Trajectories under Concept Drift. In Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, and Michalis Vazirgiannis (Eds.), Machine Learning and Knowledge Discovery in Databases, (6912):261-276, Springer Berlin Heidelberg, 2011. URL