Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic

In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available

Hybridization of Bayesian networks and belief functions to assess risk. Application to aircraft deconstruction

This paper aims to present a study on knowledge management for the disassembly of end-of-life aircraft. We propose a model using Bayesian networks to assess risk and present three approaches to integrate the belief functions standing for the representation of fuzzy and uncertain knowledge

Uncertain Knowledge Reasoning Based on the Fuzzy Multi-Entity Bayesian Network

With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this paper, we present a novel reasoning framework based on the representation of fuzzy PR-OWL. Firstly, the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning, incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory, and introduces fuzzy PR-OWL, an Ontology language based on OWL2. Fuzzy PR-OWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation. Secondly, the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm. The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL. After that, the reasoning process, including the SSFBN structure algorithm, data fuzzification, reasoning of fuzzy rules, and fuzzy belief propagation, is scheduled. Finally, compared with the classical algorithm from the aspect of accuracy and time complexity, our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity, which proves the feasibility and validity of our solution to represent and reason uncertain information

Об одном эффективном алгоритме распространения вероятностей в нечетких байесовских сетях доверия

Рассмотрена общая схема алгоритма распространения вероятностей для байесовских сетей. Построен унифицированный алгоритм распространения доверия для нечетких байесовских сетей доверия, который базируется на новых принципах обхода узлового дерева, является более прозрачным и быстродействующим. Описана структура этого алгоритма и исследованы условия его корректной работы. Описаны результаты моделирования основных операций с размытыми потенциалами, заданными над нечеткими байесовскими сетями доверия.Розглянута загальна схема алгоритма розповсюдження ймовірностей над нечіткими байєсівськими мережами довіри. Побудовано уніфікований алгоритм розповсюдження довіри на основі розмитих потенціалів. Доведена коректність виконання цього алгоритма та вказані умови йогo застосування, за яких цей алгоритм є найбільш ефективним.A general scheme of belief propagation algorithms above the fuzzy Bayesian networks of belief is considered. The new belief propagation algorithm is built on the basis of fuzzy potentials. Correctness of this implementation of algorithms is proved and conditions of his applications at which this algorithm is most effective are indicated

О реализации информационной технологии вероятностного моделирования состояний сложных систем на базе нечетких сетей доверия

Рассмотрены ключевые вопросы компьютерной реализации информационной технологии вероятностного моделирования состояний сложных систем на базе нечетких сетей доверия. Конструктивно изложен математический аппарат, с помощью которого осуществляется анализ нечетких сетей доверия и прогнозирования состояний исследуемых систем в условиях неопределенной информации. Значительное внимание уделено методологическим, архитектурным и функциональным вопросам компьютеризации.Розглянуті ключові питання комп'ютерної реалізації інформаційної технології ймовірнісного моделювання станів складних систем на базі нечітких мереж довіри. Конструктивно викладений математичний апарат, за допомогою якого здійснюється аналіз нечітких мереж довіри та прогнозування станів досліджуваних систем в умовах невизначеної інформації. Значна увага приділена технологічним, архітектурним і функціональним питанням комп'ютеризації.The key computer realization questions of information probabilistic modeling technology of the states of complex systems are considered on the base of fuzzy belief networks. A mathematical tool by which the analysis of fuzzy belief networks and prognostication of the states of the investigated systems is carried out under the conditions of indefinite information is structurally expounded. Considerable attention is spared to the technological, architectural and functional questions of computerization

Evidential Communities for Complex Networks

Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communi-ties in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropri-ate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure

Median evidential c-means algorithm and its application to community detection

Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed. The median variant relaxes the restriction of a metric space embedding for the objects but constrains the prototypes to be in the original data set. Due to these properties, MECM could be applied to graph clustering problems. A community detection scheme for social networks based on MECM is investigated and the obtained credal partitions of graphs, which are more refined than crisp and fuzzy ones, enable us to have a better understanding of the graph structures. An initial prototype-selection scheme based on evidential semi-centrality is presented to avoid local premature convergence and an evidential modularity function is defined to choose the optimal number of communities. Finally, experiments in synthetic and real data sets illustrate the performance of MECM and show its difference to other methods

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

An Overview of Classifier Fusion Methods

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided