| 1. |
- Antonucci, Alessandro, et al.
(författare)
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Decision Making with Hierarchical Credal Sets
- 2014
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Ingår i: Information Processing and Management of Uncertainty in Knowledge-Based Systems. - Cham : Springer. - 9783319088518 - 9783319088525 ; , s. 456-465
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Konferensbidrag (refereegranskat)abstract
- We elaborate on hierarchical credal sets, which are sets of probability mass functions paired with second-order distributions. A new criterion to make decisions based on these models is proposed. This is achieved by sampling from the set of mass functions and considering the Kullback-Leibler divergence from the weighted center of mass of the set. We evaluate this criterion in a simple classification scenario: the results show performance improvements when compared to a credal classifier where the second-order distribution is not taken into account.
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| 2. |
- Bae, Juhee, et al.
(författare)
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Complex Data Analysis
- 2019
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Ingår i: Data science in Practice. - Cham : Springer. - 9783319975566 - 9783319975559 ; , s. 157-169
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Bokkapitel (refereegranskat)abstract
- Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.
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| 3. |
- Bae, Juhee, et al.
(författare)
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Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types
- 2019
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Ingår i: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018. - : IEEE Computer Society. - 9781538694503 - 9781538694510 ; , s. 89-93
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Konferensbidrag (refereegranskat)abstract
- The technology and techniques for bio-sensors are rapidly evolving. Accordingly, there is significant business interest to identify upcoming technologies and new targets for the near future. Text information from internet reflects much of the recent information and public interests that help to understand the trend of a certain field. Thus, we utilize Dirichlet process topic modeling on different media sources containing short text (e.g., blogs, news) which is able to self-adapt the learned topic space to the data. We share the observations from the domain experts on the results derived from topic modeling on wearable biosensors from multiple media sources over more than eight years. We analyze the topics on wearable devices, forecast and market analysis, and bio-sensing techniques found from our method.
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| 4. |
- Bae, Juhee, et al.
(författare)
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Towards a methodological framework to address data challenges in lake water quality predictions
- 2024
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Ingår i: 3rd International Conference on Water Management in Changing Conditions. - : European Water Association; IFAT. ; , s. 5-8
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Konferensbidrag (refereegranskat)abstract
- Climate change has impacted global temperatures, triggering extreme weather and adverse environmental effects. In Sweden, these changes have caused shifts in weather patterns, leading to disruptions in infrastructure. This, in turn, has influenced water turbidity levels, negatively impacting water quality. To tackle these issues, a study was conducted using machine learning to predict turbidity with six meteorological variables collected for two years. Our preliminary research showed a substantial influence of seasonal changes on water turbidity, especially air temperature. Identifying supporting indicators such as lagged features is crucial and considerably improved the turbidity prediction performance for two of the machine learning models used. However, the study also identified challenges like data collection and uncertainty issues. We recommend improving data collection quality with higher frequency, minimizing geographical gaps between data collection points, sharing calibration assumptions, checking the sensors regularly, and accounting for data anomalies. Understanding these challenges and their potential implications could lead to more methodological enhancements.
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| 5. |
- Boström, Henrik, et al.
(författare)
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On Evidential Combination Rules for Ensemble Classifiers
- 2008
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Ingår i: Proceedings of the 11th International Conference on Information Fusion. - : IEEE. - 9783800730926 - 9783000248832 ; , s. 553-560
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Konferensbidrag (refereegranskat)abstract
- Ensemble classifiers are known to generally perform better than each individual classifier of which they consist. One approach to classifier fusion is to apply Shafer’s theory of evidence. While most approaches have adopted Dempster’s rule of combination, a multitude of combination rules have been proposed. A number of combination rules as well as two voting rules are compared when used in conjunction with a specific kind of ensemble classifier, known as random forests, w.r.t. accuracy, area under ROC curve and Brier score on 27 datasets. The empirical evaluation shows that the choice of combination rule can have a significant impact on the performance for a single dataset, but in general the evidential combination rules do not perform better than the voting rules for this particular ensemble design. Furthermore, among the evidential rules, the associative ones appear to have better performance than the non-associative.
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| 6. |
- Boström, Henrik, et al.
(författare)
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On the Definition of Information Fusion as a Field of Research
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A more precise definition of the field of information fusion can be of benefit to researchers within the field, who may use uch a definition when motivating their own work and evaluating the contribution of others. Moreover, it can enable researchers and practitioners outside the field to more easily relate their own work to the field and more easily understand the scope of the techniques and methods developed in the field. Previous definitions of information fusion are reviewed from that perspective, including definitions of data and sensor fusion, and their appropriateness as definitions for the entire research field are discussed. Based on strengths and weaknesses of existing definitions, a novel definition is proposed, which is argued to effectively fulfill the requirements that can be put on a definition of information fusion as a field of research.
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| 7. |
- Bouguelia, Mohamed-Rafik, 1987-, et al.
(författare)
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Mode tracking using multiple data streams
- 2018
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Ingår i: Information Fusion. - Amsterdam : Elsevier BV. - 1566-2535 .- 1872-6305. ; 43, s. 33-46
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Tidskriftsartikel (refereegranskat)abstract
- Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous discovery of high-level knowledge from ubiquitous data streams. This paper introduces a method for recognition and tracking of hidden conceptual modes, which are essential to fully understand the operation of complex environments, and an important step towards building truly intelligent aware systems. We consider a scenario of analyzing usage of a fleet of city buses, where the objective is to automatically discover and track modes such as highway route, heavy traffic, or aggressive driver, based on available on-board signals. The method we propose is based on aggregating the data over time, since the high-level modes are only apparent in the longer perspective. We search through different features and subsets of the data, and identify those that lead to good clusterings, interpreting those clusters as initial, rough models of the prospective modes. We utilize Bayesian tracking in order to continuously improve the parameters of those models, based on the new data, while at the same time following how the modes evolve over time. Experiments with artificial data of varying degrees of complexity, as well as on real-world datasets, prove the effectiveness of the proposed method in accurately discovering the modes and in identifying which one best explains the current observations from multiple data streams.
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| 8. |
- Brax, Christoffer, et al.
(författare)
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Evaluating Precise and Imprecise State-Based Anomaly Detectors for Maritime Surveillance
- 2010
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Ingår i: Proceedings of the 13th International Conference on Information Fusion. - : IEEE. - 9780982443811 ; , s. Article number 5711997-
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Konferensbidrag (refereegranskat)abstract
- We extend the State-Based Anomaly Detection approach by introducing precise and imprecise anomaly detectors using the Bayesian and credal combination operators, where evidences over time are combined into a joint evidence. We use imprecision in order to represent the sensitivity of the classification regarding an object being normal or anomalous. We evaluate the detectors on a real-world maritime dataset containing recorded AIS data and show that the anomaly detectors outperform previously proposed detectors based on Gaussian mixture models and kernel density estimators. We also show that our introduced anomaly detectors perform slightly better than the State-Based Anomaly Detection approach with a sliding window.
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| 9. |
- Helldin, Tove, et al.
(författare)
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Situation Awareness in Telecommunication Networks Using Topic Modeling
- 2018
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Ingår i: 2018 21st International Conference on Information Fusion, FUSION 2018. - : IEEE. - 9780996452762 - 9780996452779 - 9781538643303 ; , s. 549-556
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Konferensbidrag (refereegranskat)abstract
- For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.
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| 10. |
- Holst, Anders, et al.
(författare)
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Eliciting structure in data
- 2019
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Ingår i: CEUR Workshop Proceedings. - Aachen : CEUR-WS.
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Konferensbidrag (refereegranskat)abstract
- This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally.
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