M. Sc. Minh Tuan Pham

Methods for Intelligent Interactive Systems (I2S)

Telefon: +49 561 804-6017
Fax: +49 561 804-6022
E-Mail: tuan.pham[at]uni-kassel[dot]de

Standort: Wilhelmshöher Allee 73
34121 Kassel

Raum: WA-altes Gebäude (WA 73), ohne Raumangabe

Publikationen

[ 2022 ]
[ 2021 ]
[ 2020 ]
[ 2016 ]

2022[ to top ]

Stream-Based Active Learning in Changing Environments under Verification Latency. Pham, Tuan. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 152–164. kassel university press, 2022.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.
@incollection{pham2022stream-based, abstract = {Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.}, author = {Pham, Tuan}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {152–164}, publisher = {kassel university press}, title = {Stream-Based Active Learning in Changing Environments under Verification Latency}, year = 2022 }
%0 Book Section %1 pham2022stream-based %A Pham, Tuan %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 152--164 %T Stream-Based Active Learning in Changing Environments under Verification Latency %X Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.

2021[ to top ]

Statistical Analysis of Pairwise Connectivity. Krempl, Georg; Kottke, Daniel; Pham, Tuan. In International Conference on Discovery Science (DS), Lecture Notes in Computer Science, bll 138–148. Springer, 2021.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{krempl2021statistical, author = {Krempl, Georg and Kottke, Daniel and Pham, Tuan}, booktitle = {International Conference on Discovery Science (DS)}, keywords = {itegpub}, pages = {138–148}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Statistical Analysis of Pairwise Connectivity}, year = 2021 }
%0 Conference Paper %1 krempl2021statistical %A Krempl, Georg %A Kottke, Daniel %A Pham, Tuan %B International Conference on Discovery Science (DS) %D 2021 %I Springer %P 138--148 %R 10.1007/978-3-030-88942-5_11 %T Statistical Analysis of Pairwise Connectivity
scikit-activeml: A Library and Toolbox for Active Learning Algorithms. Kottke, Daniel; Herde, Marek; Minh, Tuan Pham; Benz, Alexander; Mergard, Pascal; Roghman, Atal; Sandrock, Christoph; Sick, Bernhard. In Preprints, bl 2021030194. 2021.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.
@article{kottke2021scikit, abstract = {Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.}, author = {Kottke, Daniel and Herde, Marek and Minh, Tuan Pham and Benz, Alexander and Mergard, Pascal and Roghman, Atal and Sandrock, Christoph and Sick, Bernhard}, journal = {Preprints}, keywords = {itegpub}, pages = 2021030194, title = {{scikit-activeml}: {A} Library and Toolbox for Active Learning Algorithms}, year = 2021 }
%0 Journal Article %1 kottke2021scikit %A Kottke, Daniel %A Herde, Marek %A Minh, Tuan Pham %A Benz, Alexander %A Mergard, Pascal %A Roghman, Atal %A Sandrock, Christoph %A Sick, Bernhard %D 2021 %J Preprints %P 2021030194 %T scikit-activeml: A Library and Toolbox for Active Learning Algorithms %U https://www.preprints.org/manuscript/202103.0194/ %X Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.

2020[ to top ]

Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. Pham Minh, T.; Kottke, D.; Tsarenko, A.; Gruhl, C.; Sick, B. In International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{phamminh2020improving, author = {{Pham Minh}, T. and {Kottke}, D. and {Tsarenko}, A. and {Gruhl}, C. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, publisher = {IEEE}, title = {Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning}, year = 2020 }
%0 Conference Paper %1 phamminh2020improving %A Pham Minh, T. %A Kottke, D. %A Tsarenko, A. %A Gruhl, C. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2020 %I IEEE %R 10.1109/ijcnn48605.2020.9206873 %T Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning

2016[ to top ]

Probabilistic Active Learning for Active Class Selection. Kottke, D.; Krempl, G.; Stecklina, M.; Styp von Rekowski, C.; Sabsch, T.; Pham Minh, T.; Deliano, M.; Spiliopoulou, M.; Sick, B. In Workshop on the Future of Interactive Learning Machines, NIPS, bll 1–9. Barcelona, Spain, 2016.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{kottke2016probabilistic, address = {Barcelona, Spain}, author = {Kottke, D. and Krempl, G. and Stecklina, M. and {Styp von Rekowski}, C. and Sabsch, T. and Pham Minh, T. and Deliano, M. and Spiliopoulou, M. and Sick, B.}, booktitle = {Workshop on the Future of Interactive Learning Machines, NIPS}, keywords = {imported}, pages = {1–9}, title = {Probabilistic Active Learning for Active Class Selection}, year = 2016 }
%0 Conference Paper %1 kottke2016probabilistic %A Kottke, D. %A Krempl, G. %A Stecklina, M. %A Styp von Rekowski, C. %A Sabsch, T. %A Pham Minh, T. %A Deliano, M. %A Spiliopoulou, M. %A Sick, B. %B Workshop on the Future of Interactive Learning Machines, NIPS %C Barcelona, Spain %D 2016 %P 1--9 %T Probabilistic Active Learning for Active Class Selection