| 1. |
- Dasari, Siva Krishna, 1988-, et al.
(författare)
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Active Learning to Support In-situ Process Monitoring in Additive Manufacturing
- 2021
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Ingår i: Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. - : IEEE. - 9781665443371 ; , s. 1168-1173
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Konferensbidrag (refereegranskat)abstract
- This paper aims to address data labelling issues in process data to support in-situ process monitoring of additive manufactured components. For this, we adopted an active learning (AL) approach to minimise the manual effort for data labelling for classification models. In this study, we present an approach that utilises pre-trained models to extract deep features from images, and clustering and query by committee sampling to select the representative samples to build defect classification models. We conduct quantitative experiments to evaluate the proposed method's performance and compare it with other selected state-of-the-art AL approaches using a dataset of additive manufacturing (AM) and a publicly available dataset. The experimental results show that the proposed approach outperforms AL with committee based sampling, and AL with clustering and random sampling. The results of the statistical significance test show that there is a significant difference between the studied AL approaches. Hence, the proposed AL approach can be considered an alternative method to reduce labelling costs when building defects classification models, whose generalizability is most likely plausible.
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| 2. |
- Dust, Lukas, et al.
(författare)
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Federated Fuzzy Learning with Imbalanced Data
- 2021
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Ingår i: Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665443371 ; , s. 1130-1137
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Konferensbidrag (refereegranskat)abstract
- Federated learning (FL) is an emerging and privacy-preserving machine learning technique that is shown to be increasingly important in the digital age. The two challenging issues for FL are: (1) communication overhead between clients and the server, and (2) volatile distribution of training data such as class imbalance. The paper aims to tackle these two challenges with the proposal of a federated fuzzy learning algorithm (FFLA) that can be used for data-based construction of fuzzy classification models in a distributed setting. The proposed learning algorithm is fast and highly cheap in communication by requiring only two rounds of interplay between the server and clients. Moreover, FFLA is empowered with an an imbalance adaptation mechanism so that it remains robust against heterogeneous distributions of data and class imbalance. The efficacy of the proposed learning method has been verified by the simulation tests made on a set of balanced and imbalanced benchmark data sets.
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| 3. |
- Jasarevic, Mirza, et al.
(författare)
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Understanding Traffic Cruising Causation Via Parking Data Enhancement
- 2021
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Ingår i: 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. - : IEEE. - 9781665443371 ; , s. 1521-1528
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Konferensbidrag (refereegranskat)abstract
- This study is devoted to understanding traffic cruising causation through exploring and enhancing parking data. Five recent (2017-2020) studies modeling parking congestion relied on occupancy as their only parking lot feature, then compared modeling techniques using this feature, to find the best performance. However, recently some computer scientists pointed out that it is more effective for the computer science community to focus more on data preparation for performance improvements, rather than exclusively comparing modeling techniques. This inspired us to add more parking lot features and evaluate them, to investigate how they should be composed into a congestion score, acting as a more accurate picture of reality. The score is then compared to the performance of a version where occupancy is the only parking lot feature. An experimental case study is designed in three parts. The first measures how the features should be summed into a score according to drivers' expectations. The second analyzes how much data can be reused from the real data, and whether spatial or temporal comparisons are better for data synthesis of parking data. The third part compares the performance of the score against the occupancy-only version using k-means clustering algorithm and dynamic time warping distance. The experimental results show performance improvements in all spatial and temporal categories, and increasing improvement as the sample sizes grow. © 2021 IEEE.
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| 4. |
- Tegen, Agnes, et al.
(författare)
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Active Learning and Machine Teaching for Online Learning : A Study of Attention and Labelling Cost
- 2021
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Ingår i: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665443371 - 9781665443388
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Konferensbidrag (refereegranskat)abstract
- Interactive Machine Learning (ML) has the potential to lower the manual labelling effort needed, as well as increase classification performance by incorporating a human-in-the loop component. However, the assumptions made regarding the interactive behaviour of the human in experiments are often not realistic. Active learning typically treats the human as a passive, but always correct, participant. Machine teaching provides a more proactive role for the human, but generally assumes that the human is constantly monitoring the learning process. In this paper, we present an interactive online framework and perform experiments to compare active learning, machine teaching and combined approaches. We study not only the classification performance, but also the effort (to label samples) and attention (to monitor the ML system) required of the human. Results from experiments show that a combined approach generally performs better with less effort compared to active learning and machine teaching. With regards to attention, the best performing strategy varied depending on the problem setup.
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