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This paper describes a methodology for incorporating human observations into a hard+soft information fusion process for counterinsurgency intelligence analysis. The goal of incorporating human observations into the information fusion process is important as it extends the ability of the fusion algorithms to associate and merge disparate pieces of information by allowing for information collected from...
We have recently introduced new generative semi supervised mixtures with more fine-grained class label generation mechanisms than previous methods. Our models combine advantages of semi supervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieves accurate classification in the vicinity of labeled samples. Our...
Cooperative spectrum sensing for cognitive radio is recently being studied to minimize uncertainty in primary user detection. In order to improve the detection probability under a sustainable false alarm probability, a reliable scheme for cooperative spectrum sensing based on double threshold energy detection and Dempster-Shafer (D-S) theory is proposed in this paper. In the algorithm, the double...
This paper proposes a soft computing approach to manage uncertainty and rule discovery by reasoning over inconsistent, incomplete and fragmentary information using dominance-based rough set theories. A methodological and computational basis is illustrated in a sensor network application scenario of a forest fire detection system.
The increasing demand for dealing with uncertainty in data has led to the development of effective and efficient approaches in the data management and mining contexts. Clustering uncertain data objects has particularly attracted great attention in the data mining community. Most existing clustering methods however have urgently to come up with a number of issues, some of which are related to a poor...
Recommender system is one of the most effective technologies to deal with information overload, which has been used in a lot of business systems. Historically, many recommender systems take much focus on prediction accuracy. However, despite their pretty accuracy, they may not be useful to users. A user's preference is full of uncertainty, including randomness and fuzziness. Unfortunately, a fixed...
The development and use of many diverse ontologies to support the representational needs of different sources and different contexts is common and necessary. However, the increased sharing of databases implementing heterogeneous ontologies pose the problem of ontological alignment. Ontology alignment typically consists of manual operations from users with different experiences and understandings and...
This work presents an application of the novel theory of rule based networks for building models of processes characterised by uncertainty, non-linearity, modular structure and internal interactions. The application of the theory is demonstrated for a flotation process in the context of converting a multiple rule based system into an equivalent single rule based system by linguistic composition of...
Map digitization is an important source of spatial data, and its process of production is complicated, the error generated by each step will influence the outcome of data quality. First, from perspective datasource, hardware, software and personnel factors related to map digitizing, the paper discussed the uncertainty; After that, an uniform spatial data quality model is built. Then starting from...
The rough fuzzy sets (RFS) is a combination granular computing model with rough sets and fuzzy sets. Its uncertainty includes roughess, rough entropy, fuzziness and fuzzy entropy, etc.. In this paper, the changes of roughness, cut-set and fuzziness are discussed according to the knowledge granularity in different knowledge granularity levels in apporiximation spaces of rough fuzzy sets. Hence, the...
In view of the problems existing in the prediction methods of coal and gas outburst, a method for prediction of coal and gas outburst based on multi-agent information fusion is proposed. In the method, considering the measured data relevant to many influence factors, a multi-agent information fusion model for rapid, dynamic and accurate prediction of coal and gas outburst is given, Dempster-Shafer...
A distributed receding horizon filtering for linear discrete-time systems with uncertainties is presented. The choice of receding horizon strategy makes the estimation fusion algorithms robust against dynamic model uncertainties. All distributed fusion algorithms are based on the fusion formulas which represent weighted sums of local receding horizon Kalman estimates with matrix weights. The fusion...
Creation of an effective metrics and estimation program is an important but daunting step for the maturing software development organization This paper outlines a roadmap for implementing a process that establishes a program that will reap a large portion of the benefits early in the process with a minimum of implementation effort and cost This process includes a mechanism to improve software estimation...
One common approach to active learning is to iteratively train a single classifier by choosing data points based on its uncertainty, but it is nontrivial to design uncertainty measures unbiased by the choice of classifier. Query by committee suggests that given an ensemble of diverse but accurate classifiers, the most informative data points are those that cause maximal disagreement among the predictions...
In the environment with objects moving randomly, the positions of moving objects can be modeled as a range of possible values, associated with a probability density function. Data mining of such positions of uncertain moving objects attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability are presented and a density-based...
This paper explores a sensor fusion method within Smart Homes to be used to monitor human activities in addition to managing uncertainty in sensor based readings. A case study has shown that the Dempster-Shafer theory of evidence can incorporate the uncertainty derived from the sensor errors and the sensor context and infer the activity. The results from this work show that this method can detect...
In this paper, we propose a method to derive and model data uncertainty from imprecise data. We view data imprecision and errors as the outcome of the precise data exposed to some uncertain channels, and our scheme is to directly derive the data uncertainty model from imprecise data, such that the derived data uncertainty information may be integrated into the succeeding mining process. To achieve...
One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, providing training data is very expensive in terms of human time and effort. In this paper we propose an active learning approach to tackle the problem. Instead of passively accepting random training examples, the active...
Cloud model is an advanced theory for solving uncertainty problems. Taking the uncertainty of images into account, this paper proposes an object detection algorithm based on the cloud model. First, adopt cloud model theory to transform the imagepsilas qualitative model to its quantitative model. Then, use climbing policy to get different level concepts which represent different level objects. At last,...
In recent years there has been a growing interest in clustering uncertain data. In contrast to traditional, "sharp" data representation models, uncertain data objects can be represented in terms of an uncertainty region over which a probability density function (pdf) is defined. In this context, the focus has been mainly on partitional and density-based approaches, whereas hierarchical clustering...
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