M. Sc. Minh Tuan Pham

2022 [ to top ]

1.
Pham, T.: Stream-Based Active Learning in Changing Environments under Verification Latency. In: Tomforde, S. en Krupitzer, C. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2021. 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 = {imported isac-www 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 ]

1.
Pham, T., Kottke, D., Krempl, G., Sick, B.: Stream-Based Active Learning for Sliding Windows Under Verification Latency. Machine Learning. (2021).
[ BibTeX ] [ EndNote ]
 
@article{pham2021stream, author = {Pham, Tuan and Kottke, Daniel and Krempl, Georg and Sick, Bernhard}, journal = {Machine Learning}, keywords = {imported itegpub}, note = {(accepted)}, publisher = {Springer}, title = {Stream-Based Active Learning for Sliding Windows Under Verification Latency}, year = 2021 }
%0 Journal Article %1 pham2021stream %A Pham, Tuan %A Kottke, Daniel %A Krempl, Georg %A Sick, Bernhard %D 2021 %I Springer %J Machine Learning %R 10.1007/s10994-021-06099-z %T Stream-Based Active Learning for Sliding Windows Under Verification Latency
2.
Krempl, G., Kottke, D., Pham, T.: Statistical Analysis of Pairwise Connectivity. International Conference on Discovery Science (DS). 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 = {imported 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
3.
Kottke, D., Herde, M., Minh, T.P., Benz, A., Mergard, P., Roghman, A., Sandrock, C., Sick, B.: scikit-activeml: A Library and Toolbox for Active Learning Algorithms. Preprints. 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 = {imported isac-www 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 ]

1.
Pham Minh, T., Kottke, D., Tsarenko, A., Gruhl, C., Sick, B.: Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. 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 = {imported 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

2019 [ to top ]

1.
Knauer, U., Styp von Rekowski, C., Stecklina, M., Krokotsch, T., Pham Minh, T., Hauffe, V., Kilias, D., Ehrhardt, I., Sagischewski, H., Chmara, S., Seiffert, U.: Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers. Remote Sensing. 11, 2788 (2019).
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ]
 
In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).
@article{knauer2019tree, abstract = {In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).}, author = {Knauer, Uwe and Styp von Rekowski, Cornelius and Stecklina, Marianne and Krokotsch, Tilman and Pham Minh, Tuan and Hauffe, Viola and Kilias, David and Ehrhardt, Ina and Sagischewski, Herbert and Chmara, Sergej and Seiffert, Udo}, journal = {Remote Sensing}, keywords = {imported}, number = 23, pages = 2788, publisher = {MDPI}, title = {Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers}, volume = 11, year = 2019 }
%0 Journal Article %1 knauer2019tree %A Knauer, Uwe %A Styp von Rekowski, Cornelius %A Stecklina, Marianne %A Krokotsch, Tilman %A Pham Minh, Tuan %A Hauffe, Viola %A Kilias, David %A Ehrhardt, Ina %A Sagischewski, Herbert %A Chmara, Sergej %A Seiffert, Udo %D 2019 %I MDPI %J Remote Sensing %N 23 %P 2788 %R 10.3390/rs11232788 %T Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers %U https://www.mdpi.com/2072-4292/11/23/2788 %V 11 %X In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).

2016 [ to top ]

1.
Kottke, D., Krempl, G., Stecklina, M., Styp von Rekowski, C., Sabsch, T., Pham Minh, T., Deliano, M., Spiliopoulou, M., Sick, B.: Probabilistic Active Learning for Active Class Selection. 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