TY - CHAP A1 - Heusinger, Moritz A1 - Schleif, Frank-Michael ED - Rutkowski, Leszek ED - Scherer, Rafal ED - Korytkowski, Marcin ED - Pedrycz, Witold ED - Tadeusiewicz, Ryszard ED - Zurada, Jacek M. T1 - Random Projection in the Presence of Concept Drift in Supervised Environments T2 - Artificial Intelligence and Soft Computing - 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I Y1 - 2020 U6 - https://doi.org/10.1007/978-3-030-61401-0_48 SP - 514 EP - 524 ER - TY - CHAP A1 - Heusinger, Moritz A1 - Schleif, Frank-Michael T1 - Reactive Concept Drift Detection Using Coresets Over Sliding Windows T2 - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, Canberra, Australia, December 1-4, 2020 Y1 - 2020 U6 - https://doi.org/10.1109/SSCI47803.2020.9308521 SP - 1350 EP - 1355 ER - TY - JOUR A1 - Heusinger, Moritz T1 - Dimensionality reduction in the context of dynamic social media data streams JF - Evolving Systems N2 - In recent years social media became an important part of everyday life for many people. A big challenge of social media is, to find posts, that are interesting for the user. Many social networks like Twitter handle this problem with so-called hashtags. A user can label his own Tweet (post) with a hashtag, while other users can search for posts containing a specified hashtag. But what about finding posts which are not labeled by the creator? We provide a way of completing hashtags for unlabeled posts using classification on a novel real-world Twitter data stream. New posts will be created every second, thus this context fits perfectly for non-stationary data analysis. Our goal is to show, how labels (hashtags) of social media posts can be predicted by stream classifiers. In particular, we employ random projection (RP) as a preprocessing step in calculating streaming models. Also, we provide a novel real-world data set for streaming analysis called NSDQ with a comprehensive data description. We show that this dataset is a real challenge for state-of-the-art stream classifiers. While RP has been widely used and evaluated in stationary data analysis scenarios, non-stationary environments are not well analyzed. In this paper, we provide a use case of RP on real-world streaming data, especially on NSDQ dataset. We discuss why RP can be used in this scenario and how it can handle stream-specific situations like concept drift. We also provide experiments with RP on streaming data, using state-of-the-art stream classifiers like adaptive random forest and concept drift detectors. Additionally, we experimentally evaluate an online principal component analysis (PCA) approach in the same fashion as we do for RP. To obtain higher dimensional synthetic streams, we use random Fourier features (RFF) in an online manner which allows us, to increase the number of dimensions of low dimensional streams. KW - social media KW - data Y1 - 2021 UR - https://doi.org/10.1007/s12530-021-09396-z SN - 1868-6486 ER - TY - JOUR A1 - Heusinger, Moritz T1 - Passive concept drift handling via variations of learning vector quantization JF - Neural Computing and Applications N2 - Concept drift is a change of the underlying data distribution which occurs especially with streaming data. Besides other challenges in the field of streaming data classification, concept drift has to be addressed to obtain reliable predictions. Robust Soft Learning Vector Quantization as well as Generalized Learning Vector Quantization has already shown good performance in traditional settings and is modified in this work to handle streaming data. Further, momentum-based stochastic gradient descent techniques are applied to tackle concept drift passively due to increased learning capabilities. The proposed work is tested against common benchmark algorithms and streaming data in the field and achieved promising results. KW - concept drift KW - data Y1 - 2020 UR - https://doi.org/10.1007/s00521-020-05242-6 SN - 1433-3058 VL - 34 SP - 89 EP - 100 ER - TY - CHAP A1 - Heusinger, Moritz A1 - Raab, Christoph A1 - Schleif, Frank-Michael ED - Vellido, Alfredo ED - Gibert, Karina ED - Angulo, Cecilio ED - David Martı́n-Guerrero, José T1 - Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization T2 - Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization - Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019 Y1 - 2019 U6 - https://doi.org/10.1007/978-3-030-19642-4_20 SP - 200 EP - 209 ER - TY - CHAP A1 - Raab, Christoph A1 - Heusinger, Moritz A1 - Schleif, Frank-Michael T1 - Reactive Soft Prototype Computing for frequent reoccurring Concept Drift T2 - 27th European Symposium on Artificial Neural Networks, ESANN 2019, Bruges, Belgium, April 24-26, 2019 Y1 - 2019 ER - TY - JOUR A1 - Raab, Christoph A1 - Heusinger, Moritz A1 - Schleif, Frank-Michael T1 - Reactive Soft Prototype Computing for Concept Drift Streams JF - CoRR Y1 - 2020 VL - abs/2007.05432 ER - TY - CHAP A1 - Heusinger, Moritz A1 - Schleif, Frank-Michael ED - Rojas, Ignacio ED - Joya, Gonzalo ED - Català, Andreu T1 - Classification in Non-stationary Environments Using Coresets over Sliding Windows T2 - Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16-18, 2021, Proceedings, Part I Y1 - 2021 U6 - https://doi.org/10.1007/978-3-030-85030-2_11 SP - 126 EP - 137 ER - TY - CHAP A1 - Heusinger, Moritz A1 - Schleif, Frank-Michael ED - Rutkowski, Leszek ED - Scherer, Rafal ED - Korytkowski, Marcin ED - Pedrycz, Witold ED - Tadeusiewicz, Ryszard ED - Zurada, Jacek M. T1 - A Streaming Approach to the Core Vector Machine T2 - Artificial Intelligence and Soft Computing - 21st International Conference, ICAISC 2022, Zakopane, Poland, June 19-23, 2022, Proceedings, Part II Y1 - 2023 U6 - https://doi.org/10.1007/978-3-031-23480-4_8 SP - 91 EP - 101 ER - TY - CHAP A1 - Heusinger, Moritz A1 - Raab, Christoph A1 - Rossi, Fabrice A1 - Schleif, Frank-Michael T1 - Federated Learning - Methods, Applications and beyond T2 - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021, Online event (Bruges, Belgium), October 6-8, 2021 Y1 - 2021 U6 - https://doi.org/10.14428/esann/2021.ES2021-4 ER -