| 1. |
- Boström, Henrik, et al.
(author)
-
On Evidential Combination Rules for Ensemble Classifiers
- 2008
-
In: Proceedings of the 11th International Conference on Information Fusion. - : IEEE. - 9783800730926 - 9783000248832 ; , s. 553-560
-
Conference paper (peer-reviewed)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.
|
|
| 2. |
- Boström, Henrik, et al.
(author)
-
On the Definition of Information Fusion as a Field of Research
- 2007
-
Reports (other academic/artistic)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.
|
|
| 3. |
- Ericson, Stefan, et al.
(author)
-
Information Fusion for Autonomous Robotic Weeding
- 2009
-
In: INFORMATIK 2009. - : Köllen Druck + Verlag GmbH. - 9783885792482 ; , s. 2461-2473
-
Conference paper (peer-reviewed)abstract
- Information fusion has a potential applicability to a multitude of differentapplications. Still, the JDL model is mostly used to describe defense applications.This paper describes the information fusion process for a robot removing weed ina field. We analyze the robotic system by relating it to the JDL model functions.The civilian application we consider here has some properties which differ from thetypical defense applications: (1) indifferent environment and (2) a predictable andstructured process to achieve its objectives. As a consequence, situation estimatestend to deal with internal properties of the robot and its mission progress (throughmission state transition) rather than external entities and their relations. Nevertheless, the JDL model appears useful for describing the fusion activities of the weeding robot system. We provide an example of how state transitions may be detected and exploited using information fusion and report on some initial results. An additional finding is that process refinement for this type of application can be expressed in terms of a finite state machine.
|
|
| 4. |
- Hilletofth, Per, et al.
(author)
-
Agent-Based Simulation Fusion for Improved Decision Making for Service Operations
- 2009
-
In: Proceedings of the 12th International Conference on Information Fusion. - : IEEE. - 9780982443804
-
Conference paper (peer-reviewed)abstract
- We use agent-based modeling and simulation to fuse data from multiple sources to estimate the state of some system properties. This implies that the real system of interest is modeled and simulated using agent principles. Using Monte-Carlo simulation, we estimate the values of some decision-relevant numerical properties, such as utilization of resources and service levels, as a decision support for a Maintenance Service Provider. Our initial results indicate that this kind of fusion of information sources can improve the understanding of the problem domain (e.g. to what degree some critical properties influence service operations) and also generate a basis for decision-making.
|
|
| 5. |
- Hilletofth, Per, et al.
(author)
-
Agent-based simulation fusion for improved decision making for service operations
- 2009
-
In: Proceedings of the 12th International Conference on Information Fusion. - Seattle, US. - 9780982443804 ; , s. 998-1005
-
Conference paper (peer-reviewed)abstract
- We use agent-based modeling and simulation to fuse data from multiple sources to estimate the state of some system properties. This implies that the real system of interest is modeled and simulated using agent principles. Using Monte-Carlo simulation, we estimate the values of some decision-relevant numerical properties. We use the estimated properties, such as utilization of resources and service levels, as a decision support for a Maintenance Service Provider. Our initial results indicate that this kind of fusion of information sources can improve the understanding of the problem domain (e.g. to what degree some critical properties influence service operations) and also generate a basis for decision-making.
|
|
| 6. |
- Johansson, Ronnie, et al.
(author)
-
A Study on Class-Specifically Discounted Belief for Ensemble Classifiers
- 2008
-
In: 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. - : IEEE. - 9781424421442 - 9781424421435 ; , s. 614-619
-
Conference paper (peer-reviewed)abstract
- Ensemble classifiers are known to generally perform better than their constituent classifiers. Whereas a lot of work has been focusing on the generation of classifiers for ensembles, much less attention has been given to the fusion of individual classifier outputs. One approach to fuse the outputs is to apply Shafer’s theory of evidence, which provides a flexible framework for expressing and fusing beliefs. However, representing and fusing beliefs is non-trivial since it can be performed in a multitude of ways within the evidential framework. In a previous article, we compared different evidential combination rules for ensemble fusion. The study involved a single belief representation which involved discounting (i.e., weighting) the classifier outputs with classifier reliability. The classifier reliability was interpreted as the classifier’s estimated accuracy, i.e., the percentage of correctly classified examples. However, classifiers may have different performance for different classes and in this work we assign the reliability of a classifier output depending on the classspecific reliability of the classifier. Using 27 UCI datasets, we compare the two different ways of expressing beliefs and some evidential combination rules. The result of the study indicates that there is indeed an advantage of utilizing class-specific reliability compared to accuracy in an evidential framework for combining classifiers in the ensemble design considered.
|
|
| 7. |
- Johansson, Ronnie, 1975-
(author)
-
Large-Scale Information Acquisition for Data and Information Fusion
- 2006
-
Doctoral thesis (other academic/artistic)abstract
- The purpose of information acquisition for data and information fusion is to provide relevant and timely information. The acquired information is integrated (or fused) to estimate the state of some environment. The success of information acquisition can be measured in the quality of the environment state estimates generated by the data and information fusion process. In this thesis, we introduce and set out to characterise the concept of large-scale information acquisition. Our interest in this subject is justified both by the identified lack of research on a holistic view on data and information fusion, and the proliferation of networked sensors which promises to enable handy access to a multitude of information sources. We identify a number of properties that could be considered in the context of large-scale information acquisition. The sensors used could be large in number, heterogeneous, complex, and distributed. Also, algorithms for large-scale information acquisition may have to deal with decentralised control and multiple and varying objectives. In the literature, a process that realises information acquisition is frequently denoted sensor management. We, however, introduce the term perception management instead, which encourages an agent perspective on information acquisition. Apart from explictly inviting the wealth of agent theory research into the data and information fusion research, it also highlights that the resource usage of perception management is constrained by the overall control of a system that uses data and information fusion. To address the challenges posed by the concept of large-scale information acquisition, we present a framework which highlights some of its pertinent aspects. We have implemented some important parts of the framework. What becomes evident in our study is the innate complexity of information acquisition for data and information fusion, which suggests approximative solutions. We, furthermore, study one of the possibly most important properties of large-scale information acquisition, decentralised control, in more detail. We propose a recurrent negotiation protocol for (decentralised) multi-agent coordination. Our approach to the negotiations is from an axiomatic bargaining theory perspective; an economics discipline. We identify shortcomings of the most commonly applied bargaining solution and demonstrate in simulations a problem instance where it is inferior to an alternative solution. However, we can not conclude that one of the solutions dominates the other in general. They are both preferable in different situations. We have also implemented the recurrent negotiation protocol on a group of mobile robots. We note some subtle difficulties with transferring bargaining solutions from economics to our computational problem. For instance, the characterising axioms of solutions in bargaining theory are useful to qualitatively compare different solutions, but care has to be taken when translating the solution to algorithms in computer science as some properties might be undesirable, unimportant or risk being lost in the translation.
|
|
| 8. |
- Johansson, Ronnie M., et al.
(author)
-
Particle filter-based information acquisition for robust plan recognition
- 2005
-
In: 2005 7th International Conference on Information Fusion (FUSION), Vols 1 and 2. - 0780392868 ; , s. 183-190
-
Conference paper (peer-reviewed)abstract
- Plan recognition generates high-level information of opponents' plans, typically a probability distribution over a set of plausible plans. Estimations of plans, are in our work, made at different decision-levels, both company-level and the subsumed platoon-level. Naturally, successful plan recognition is heavily dependent on the data that is supplied, and, hence, sensor management is a necessity. A key feature of the sensor management discussed here is that it is driven by the information need of the plan recognition process. In our research, we have presented a general framework for connecting information need to sensor management. In our framework implementation, an essential part is the prioritization of sensing tasks, which is necessary to efficiently utilize limited sensing resources. In our first implementation, the priorities were calculated from, for instance, the estimated threats of opponents (as a function of plan estimates), the distance to the opponent, and the uncertainty in its position. In this article, we add a particle filter method to more carefully represent the uncertainty in the opponent state estimate to make prioritization more well founded and, ultimately, to achieve robust plan recognition. By using the particle filter we can obtain more reliable state estimates (through the particle filter's ability to represent complex probability distributions) and also a statistically based threat variation (through Monte-Carlo simulation). The state transition model of the particle filter can also be used to predict future states to direct sensors with a time delay (a common property of large-scale sensing systems), such as sensors mounted on UAVs that have to travel some distance to make a measurement.
|
|
| 9. |
- Karlsson, Alexander, et al.
(author)
-
An Empirical Comparison of Bayesian and Credal Networks for Dependable High-Level Information Fusion
- 2008
-
In: Proceedings of the 11th International Conference on Information Fusion. - : IEEE. - 9783000248832 - 9783800730926 ; , s. 1359-1366
-
Conference paper (peer-reviewed)abstract
- Bayesian networks are often proposed as a method for high-level information fusion. However, a Bayesian network relies on strong assumptions about the underlying probabilities. In many cases it is not realistic to require such precise probability assessments. We show that there exists a significant set of problems where credal networks outperform Bayesian networks, thus enabling more dependable decision making for this type of problems. A credal network is a graphical probabilistic method that utilizes sets of probability distributions, e.g., interval probabilities, for representation of belief. Such a representation allows one to properly express epistemic uncertainty, i.e., uncertainty that can be reduced if more information becomes available. Since reducing uncertainty has been proposed as one of the main goals of information fusion, the ability to represent epistemic uncertainty becomes an important aspect in all fusion applications.
|
|
| 10. |
- Karlsson, Alexander, et al.
(author)
-
Imprecise Probability as an Approach to Improved Dependability in High-Level Information Fusion
- 2008
-
In: Interval / Probabilistic Uncertainty and Non-Classical Logics. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540776635 ; , s. 70-84
-
Conference paper (other academic/artistic)abstract
- The main goal of information fusion can be seen as improving human or automatic decision-making by exploiting diversities in information from multiple sources. High-level information fusion aims specifically at decision support regarding situations, often expressed as “achieving situation awareness”. A crucial issue for decision making based on such support is trust that can be defined as “accepted dependence”, where dependence or dependability is an overall term for many other concepts, e.g., reliability. This position paper reports on ongoing and planned research concerning imprecise probability as an approach to improved dependability in high-level information fusion. We elaborate on high-level information fusion from a generic perspective and a partial mapping from a taxonomy of dependability to high-level information fusion is presented. Three application domains: defense, manufacturing, and precision agriculture, where experiments are planned to be implemented are depicted. We conclude that high-level information fusion as an application-oriented research area, where precise probability (Bayesian theory) is commonly adopted, provides an excellent evaluation ground for imprecise probability.
|
|
| 11. |
- Karlsson, Alexander, et al.
(author)
-
On the Behavior of the Robust Bayesian Combination Operator and the Significance of Discounting
- 2009
-
In: ISIPTA ’09. - : Society for Imprecise Probability. ; , s. 259-268
-
Conference paper (peer-reviewed)abstract
- We study the combination problem for credal sets via the robust Bayesian combination operator. We extend Walley's notion of degree of imprecision and introduce a measure for degree of conflict between two credal sets. Several examples are presented in order to explore the behavior of the robust Bayesian combination operator in terms of imprecision and conflict. We further propose a discounting operator that suppresses a source given an interval of reliability weights, and highlight the importance of using such weights whenever additional information about the reliability of a source is available.
|
|
| 12. |
- Lätt, Jimmy, et al.
(author)
-
In vivo visualization of displacement-distribution-derived parameters in q-space imaging.
- 2008
-
In: Magnetic Resonance Imaging. - : Elsevier BV. - 1873-5894 .- 0730-725X. ; 26:1, s. 77-87
-
Journal article (peer-reviewed)abstract
- Objective This study aimed to explore the potential of in vivo q-space imaging in the differentiation between different cerebral water components. Materials and Methods Diffusion-weighted imaging was performed in six directions with 32 equally spaced q values and a maximum b value of 6600 s/mm2. The shape of the signal-attenuation curve and the displacement propagator were examined and compared with a normal distribution using the kurtosis parameter. Maps displaying kurtosis, fast and slow components of the apparent diffusion coefficients, fractional anisotropy and directional diffusion were calculated. The displacement propagator was further described by the full width at half and at tenth maximum and by the probability density of zero displacement P(0). Three healthy volunteers and three patients with previously diagnosed multiple sclerosis (MS) were examined. Results Simulations indicated that the kurtosis of a signal-attenuation curve can determine if more than one water component is present and that care must be taken to select an appropriate threshold. It was possible to distinguish MS plaques in both signal and diffusional kurtosis maps, and in one patient, plaques of different degree of demyelinization showed different behavior. Discussion Our results indicate that in vivo q-space analysis is a potential tool for the assessment of different cerebral water components, and it might extend the diagnostic interpretation of data from diffusion magnetic resonance imaging.
|
|
| 13. |
- Mellin, Jonas, et al.
(author)
-
Adaptive Application and Resource Management
- 2008
-
In: Proceedings of the second Skövde Workshop on Information fusion Topics (SWIFT 2008). - Skövde : University of Skövde. - 9789163336973 ; , s. 67-70
-
Conference paper (peer-reviewed)
|
|
| 14. |
- Mårtensson, Jan, et al.
(author)
-
Nurse-led heart failure follow-up in primary care in Sweden
- 2009
-
In: EUROPEAN JOURNAL OF CARDIOVASCULAR NURSING. - : Oxford University Press (OUP). - 1474-5151 .- 1873-1953. ; 8:2, s. 119-124
-
Journal article (peer-reviewed)abstract
- Background: Little or no knowledge is available about which service is offered to patients with heart failure in primary care. Aims: To describe nurse-led follow-up of patients with heart failure in primary care in Sweden. Methods and results: A questionnaire was sent to all primary health care centres in Sweden and 6 10 of 939 centres returned the questionnaire. Special nurses had designated time for follow-up of heart failure patients at 18% (n = 111) of the centres, compared to 93% and 78% for diabetes and obstructive lung disease. Centres with nurse-led follow-up of heart failure patients more frequently provided heart failure information (pandlt;0.001), had more doctors interested in heart failure (pandlt;0.001), more often had special care programmes (Pandlt;0.001) and had more co-operation with the hospitals around patients with heart failure (pandlt;0.01), compared to centres without such follow-up (n = 499). Conclusion: In primary care in Sweden, nurse-led follow-up is uncommon for patients with heart failure, despite being common for patients with diabetes and obstructive pulmonary disease. To improve this situation in primary care, an increased number of specially trained nurses is needed, together with further research to ensure a high quality follow-up in primary care.
|
|
| 15. |
|
|
