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The Effects of Reluctant and Fallible Users in Interactive Online Machine Learning
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0002-3155-8408
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0003-0998-6585
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-9471-8405
2020 (Engelska)Ingår i: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020) / [ed] Daniel Kottke, Georg Krempl, Vincent Lemaire, Andreas Holzinger & Adrian Calma, CEUR Workshops , 2020, s. 55-71Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

In interactive machine learning it is important to select the most informative data instances to label in order to minimize the effort of the human user. There are basically two categories of interactive machine learning. In the first category, active learning, it is the computational learner that selects which data to be labelled by the human user, whereas in the second one, machine teaching, the selection is done by the human teacher. It is often assumed that the human user is a perfect oracle, i.e., a label will always be provided in accordance with the interactive learning strategy and that this label will always be correct. In real-world scenarios however, these assumptions typically do not hold. In this work, we investigate how the reliability of the user providing labels affects the performance of online machine learning. Specifically, we study reluctance, i.e., to what extent the user does not provide labels in accordance with the strategy, and fallibility, i.e., to what extent the provided labels are incorrect. We show results of experiments on a benchmark dataset as well as a synthetically created dataset. By varying the degree of reluctance and fallibility of the user, the robustness of the different interactive learning strategies and machine learning algorithms is explored. The experiments show that there is a varying robustness of the strategies and algorithms. Moreover, certain machine learning algorithms are more robust towards reluctance compared to fallibility, while the opposite is true for others

Ort, förlag, år, upplaga, sidor
CEUR Workshops , 2020. s. 55-71
Serie
CEUR Workshop Proceedings, E-ISSN 1613-0073
Nationell ämneskategori
Programvaruteknik
Identifikatorer
URN: urn:nbn:se:mau:diva-17673OAI: oai:DiVA.org:mau-17673DiVA, id: diva2:1451764
Konferens
Interactive Adaptive Learning 2020, Ghent, Belgium, September 14th, 2020.
Tillgänglig från: 2020-07-03 Skapad: 2020-07-03 Senast uppdaterad: 2023-12-28Bibliografiskt granskad
Ingår i avhandling
1. Approaches to Interactive Online Machine Learning
Öppna denna publikation i ny flik eller fönster >>Approaches to Interactive Online Machine Learning
2020 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. The sensors of different devices might be of different types, making the fusion of data non-trivial. Moreover, the devices are often mobile, resulting in that data from a particular sensor is not always available, i.e. there is a need to handle data from a dynamic set of sensors. From a machine learning perspective, the data from the sensors arrives in a streaming fashion, i.e., online learning, as compared to many learning problems where a static dataset is assumed. Machine learning is in many cases a good approach for classification problems, but the performance is often linked to the quality of the data. Having a good data set to train a model can be an issue in general, due to the often costly process of annotating the data. With dynamic and heterogeneous data, annotation can be even more problematic, because of the ever-changing environment. This means that there might not be any, or a very small amount of, annotated data to train the model on at the start of learning, often referred to as the cold start problem.

To be able to handle these issues, adaptive systems are needed. With adaptive we mean that the model is not static over time, but is updated if there for instance is a change in the environment. By including human-in-the-loop during the learning process, which we refer to as interactive machine learning, the input from users can be utilized to build the model. The type of input used is typically annotations of the data, i.e. user input in the form of correctly labelled data points. Generally, it is assumed that the user always provides correct labels in accordance with the chosen interactive learning strategy. In many real-world applications these assumptions are not realistic however, as users might provide incorrect labels or not provide labels at all in line with the chosen strategy.

In this thesis we explore which interactive learning strategies are possible in the given scenario and how they affect performance, as well as the effect of machine learning algorithms on performance. We also study how a user who is not always reliable, i.e. that does not always provide a correct label when expected to, can affect performance. We propose a taxonomy of interactive online machine learning strategies and test how the different strategies affect performance through experiments on multiple datasets. The findings show that the overall best performing interactive learning strategy is one where the user provides labels when previous estimations have been incorrect, but that the best performing machine learning algorithm depends on the problem scenario. The experiments also show that a decreased reliability of the user leads to decreased performance, especially when there is a limited amount of labelled data.

Ort, förlag, år, upplaga, sidor
Malmö: Malmö universitet, 2020. s. 129
Serie
Studies in Computer Science ; 10
Nyckelord
Machine Learning, Interactive Machine Learning, Online Learning, Active Learning, Machine Teaching
Nationell ämneskategori
Annan data- och informationsvetenskap
Identifikatorer
urn:nbn:se:mau:diva-17433 (URN)10.24834/isbn.9789178770854 (DOI)978-91-7877-084-7 (ISBN)978-91-7877-085-4 (ISBN)
Presentation
2020-06-18, 10:15 (Engelska)
Opponent
Handledare
Forskningsfinansiär
KK-stiftelsen, 20140035
Tillgänglig från: 2020-06-09 Skapad: 2020-06-09 Senast uppdaterad: 2024-03-05Bibliografiskt granskad
2. Interactive Online Machine Learning
Öppna denna publikation i ny flik eller fönster >>Interactive Online Machine Learning
2022 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. The sensors of different devices might be mobile and of different types, i.e. there is a need to handle streaming data from a dynamic and heterogeneous set of sensors. In machine learning, the performance is often linked to the availability and quality of annotated data. Annotating data is in general costly, but it can be even more challenging if there is not any, or a very small amount of, annotated data to train the model on at the start of learning. To handle these issues, we implement interactive and adaptive systems. By including human-in-the-loop, which we refer to as interactive machine learning, the input from users can be utilized to build the model. The type of input used in interactive machine learning is typically annotations of the data, i.e. correctly labelled data points. Generally, it is assumed that the user always provides correct labels in accordance with the chosen interactive learning strategy. In many real-world applications these assumptions are not realistic however, as users might provide incorrect labels or not provide labels at all in line with the chosen strategy.

In this thesis we explore which interactive learning strategy types are possible in the given scenario and how they affect performance, as well as the effect of machine learning algorithms on the performance. We also study how a user who is not always reliable, i.e. who does not always provide a correct label when expected to, can affect performance. We propose a taxonomy of interactive online machine learning strategies and test how the different strategies affect performance through experiments on multiple datasets. Simulated experiments are compared to experiments with human participants, to verify the results. The findings show that the overall best performing interactive learning strategy is one where the user provides labels when current estimations are incorrect, but that the best performing machine learning algorithm depends on the problem scenario. The experiments also show that a decreased reliability of the user leads to decreased performance, especially when there is a limited amount of labelled data. The robustness of the machine learning algorithms differs, where e.g. Naïve Bayes classifier is better at handling a lower reliability of the user. We also present a systematic literature review on machine teaching, a subfield of interactive machine learning where the human is proactive in the interaction. The study shows that the area of machine teaching is rapidly evolving with an increased number of publications in recent years. However, as it is still maturing, there exists several open challenges that would benefit from further exploration, e.g. how human factors can affect performance.

Ort, förlag, år, upplaga, sidor
Malmö: Malmö universitet, 2022. s. 209
Serie
Studies in Computer Science ; 18
Nyckelord
Interactive Machine Learning, Active Learning, Machine Teaching, Online Learning
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-51987 (URN)10.24834/isbn.9789178772810 (DOI)978-91-7877-280-3 (ISBN)978-91-7877-281-0 (ISBN)
Disputation
2022-06-23, HS aula samt livestramas, Jan Waldenströms gata 25, Malmö, 10:00 (Engelska)
Opponent
Handledare
Anmärkning

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Malmö University's products or services. Internal or personal use of this material is permitted.

Paper VI and VII appear in dissertation as manuscripts.

Tillgänglig från: 2022-06-03 Skapad: 2022-06-02 Senast uppdaterad: 2023-09-05Bibliografiskt granskad

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