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.

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.

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.

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.

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.

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.

Determining how to utilize information acquisition resources optimally is a difficult task in the intelligence domain. Nevertheless, an intelligence analyst can expect little or no support for this from software tools today. In this paper, we describe a proof of concept implementation of a resource allocation mechanism for an intelligence analysis support system. The system uses a Bayesian network to structure intelligence requests, and the goal is to minimize the uncertainty of a variable of interest. A number of allocation strategies are discussed and evaluated through simulations.

We are interested in whether or not representing and maintaining imprecision is beneficial when combining evidences from multiple sources. We perform two experiments that contain different levels of risk and where we measure the performance of the Bayesian and credal combination operators by using a simple score function that measures the informativeness of a reported decision set. We show that the Bayesian combination operator performed on centroids of operand credal sets outperforms the credal combination operator when no risk is involved in the decision problem. We also show that if a risk component is present in the decision problem, a simple cautious decision policy for the Bayesian combination operator can be constructed that outperforms the corresponding credal decision policy.

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.

We are interested in whether or not there exist any advantages of utilizing credal set theory for the discrete state estimation problem. We present an experiment where we compare in total six different methods, three based on Bayesian theory and three on credal set theory. The results show that Bayesian updating performed on centroids of operand credal sets significantly outperforms the other methods. We analyze the result based on degree of imprecision, position of extreme points, and second-order distributions.

We address the problem of combining independent evidences from multiple sources by utilizing the Bayesian and credal combination operators. We present measures for degree of conflict and imprecision, which we use in order to characterize the behavior of the operators through a number of examples. We introduce discounting operators that can be used whenever information about the reliability of sources is available. The credal discounting operator discounts a credal set with respect to an interval of reliability weights, hence, we allow for expressing reliability of sources imprecisely. We prove that the credal discounting operator can be computed by using the extreme points of its operands. We also perform two experiments containing different levels of risk where we compare the performance of the Bayesian and credal combination operators by using a simple score function that measures the informativeness of a reported decision set. We show that the Bayesian combination operator performed on centroids of operand credal sets outperforms the credal combination operator when no risk is involved in the decision problem. We also show that if a risk component is present in the decision problem, a simple cautious decision policy for the Bayesian combination operator can be constructed that outperforms the corresponding credal decision policy.

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.

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.

This paper presents a comprehensive summary of the state-of-the-art of energy efficiency research. The literature review carried out focuses on the application of data mining and data analysis techniques to energy consumption data, as well as  descriptions  of  tools, applications and research prototypes to manage the consumption of energy. Moreover, preliminary results of the application of a clustering technique to energy consumption data illustrate the review.