Conjunctive Combination Rule Research Articles

On Computing Evidential Centroid Through Conjunctive Combination: An Impossibility Theorem

The theory of belief functions (TBF) is now a widespread framework to deal and reason with uncertain and imprecise information, in particular to solve information fusion and clustering problems. Combination functions (rules) and distances are essential tools common to both the clustering and information fusion problems in the context of TBF, which have generated considerable literature. Distances and combination between evidence corpus of TBF are indeed often used within various clustering and classification algorithms, however their interplay and connections have seldom been investigated, which is the topic of this paper. More precisely, we focus on the problem of aggregating evidence corpus to obtain a representative one, and we show through an impossibility theorem that in this case, there is a fundamental contradiction between the use of conjunctive combination rules on the one hand, and the use of distances on the other hand. Rather than adding new methodologies, such results are instrumental in guiding the user among the many methodologies that already exist. To illustrate the interest of our results, we discuss different cases where they are at play.

Study on the Location of Private Clinics Based on K-Means Clustering Method and an Integrated Evaluation Model

Appropriate location is an important prerequisite for the long-term survival and development of private medical institutions. However, in both theory and practice, the issue of location decision-making for private clinics has not been fully studied. We therefore aimed to provide a feasible scheme for the location of new private clinics. This paper combines the k-means clustering method and an integrated 2DULVs (two-dimensional uncertain language variables)-TOPSIS (technique for order preference by similarity to ideal solution)-DSCCR (Dempster-Shafer conjunctive combination rule) model to screen and evaluate all of the areas in the target region for an Internet medical company to set up offline clinics. We first created geographic grids using GIS and collected point of interest (POI) data. We then used the k-means clustering method to obtain 10 suitable grids as alternatives. Last, we established an evaluation index system and used the proposed model to rank them. The results show that grids 178, 179 and 202 are more suitable for the company to establish offline clinics in the expansion of business. The results of this study are also consistent with those of the other three fusion methods. This paper provides a beneficial attempt for private clinics to make location decisions and can be extended to the strategic decision-making of other industries or other issues.

Evidential K-NN classification with enhanced performance via optimizing a class of parametric conjunctive t-rules

Dempster's rule of combination is commonly used to pool distinct/independent bodies of evidence in the evidential k-nearest neighbor (K-NN) classifier, which sometimes limits the performance of this classifier. To solve this problem, we propose a class of parametric conjunctive combination rules based on a new family of triangular norms with selectable functions and tunable parameters. We show that the performance of the evidential K-NN classifier can be enhanced via this class of so-called parametric conjunctive t-rules when appropriate functions and parameters are selected. Numerical simulations validate our conclusions.

Information fusion-based approach for studying influence on Twitter using belief theory.

Influence in Twitter has become recently a hot research topic, since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this study, we propose a new approach for multi-level influence assessment on multi-relational networks, such as Twitter. We define a social graph to model the relationships between users as a multiplex graph where users are represented by nodes, and links model the different relations between them (e.g., retweets, mentions, and replies). We explore how relations between nodes in this graph could reveal about the influence degree and propose a generic computational model to assess influence degree of a certain node. This is based on the conjunctive combination rule from the belief functions theory to combine different types of relations. We experiment the proposed method on a large amount of data gathered from Twitter during the European Elections 2014 and deduce top influential candidates. The results show that our model is flexible enough to to consider multiple interactions combination according to social scientists needs or requirements and that the numerical results of the belief theory are accurate. We also evaluate the approach over the CLEF RepLab 2014 data set and show that our approach leads to quite interesting results.

Robust object tracking based upon adaptive multi-cue integration for video surveillance

It was well argued in literature that integrating multi-cue increases accuracy and robustness of visual tracking. Although, multi-cues object tracking using singlemodal or multimodal was explored by some of researchers, it still remains an open challenge to fuse multi-cue from different modularity under dynamic environment conditions. The aim of this research paper is to introduce a novel multi-cue object tracking framework using particle filter. In particle filter framework, our approach integrates cues while evaluating each particle instead of primitive approach of deciding cues performance in current frame based upon either a few present particles or previous state particles. First, we model our multi-cue tracking framework using Shafer's model and multi-cue data is combined using Conjunctive combination rules. The partial and total conflict among cues at particle level is redistributed efficiently using Proportional Conflict Redistribution (PCR-5) rules. In proposed model, automatic suppression/boosting of particles along with online conflict resolving facilitate resampling process for efficiently handling of particle degeneracy. Most importantly, compared to other state-of-art trackers, our proposed algorithm can handle more efficiently various dynamic environments conditions such as partial or full occlusion, illumination changes, weather, and visibility. In this manuscript, we demonstrate our proposed adaptive multi-cue fusion model on challenging benchmark video and thermal sequences and compare tracking results of our tracker with state-of- art trackers.

Map-Aided Evidential Grids for Driving Scene Understanding

Evidential grids have recently been shown to have interesting properties for mobile object perception. Possessing only partial information is a frequent situation when driving in complex urban areas, and by making use of the Dempster-Shafer framework, evidential grids are able to handle partial information efficiently. This article deals with a lidar perception scheme that is enhanced by geo-referenced maps used as an additional source of information in a multi-grid fusion framework. The paper looks at the key stages of such a data fusion process and presents an adaptation of the conjunctive combination rule for refining the analysis of conflicting information. This method relies on temporal accumulation to distinguish between stationary and moving objects, and applies contextual discounting for modeling information obsolescence. As a result, the method is able to better characterize the state of the occupied cells by differentiating moving objects, parked cars, urban infrastructure and buildings. Another advantage of this approach is its ability to separate the drivable from the non-drivable free space. Experiments carried out in real traffic conditions with a specially equipped car illustrate the performance of this approach.

New distances between bodies of evidence based on Dempsterian specialization matrices and their consistency with the conjunctive combination rule

Distances in evidence theory are useful tools for belief function approximation or clustering. Efficient approaches are found in the literature, especially full metrics taking focal element interactions into account. In this paper, another aspect is investigated: the ability to detect common evidence pertaining to two different states of belief. This requirement, as well as the previously mentioned ones, are formalized through mathematical properties. To find a belief function distance satisfying the desired properties, matrix norms based distances between Dempsterian specialization matrices are investigated. It is proved that the L1 Dempsterian matrix distance succeeds to fulfill all requirements. Interesting and unprecedented ties between the conjunctive combination rule and this distance are demonstrated. Several basic belief assignment distance experiments are proposed and interpreted thereby allowing to understand advantages and limitations of the newly introduced distances as compared to the existing ones.

New Methods of Evidence Conflict Measurement Based on Conjunctive Combination Rule

In the effective combination of conflicting evidences using the Dempster-Shafer evidence theory, the first step is to reasonably measure the conflict between evidences, but there are limitations in the existing conflict measurement methods. Two new conflict measurement methods based on conjunctive combination rule are put forword, which overcome the limitations of the existing measurement methods. They have four satisfactory properties. Firstly, new methods can measure the total conflict between any pieces of evidence simultaneously, which can satisfy the interchangeability and combinability. Secondly, they overcome the operational problem of the existing binary conflict measurement methods. Thirdly, they are more suitable for people's intuitive logic reasoning. Another, their moderate complexity are easy for project implementation. So new methods have better comprehensive effect under different evidence conditions.

A conjunctive parallelogram model for pick any/n data

This paper proposes a multidimensional generalization of Coombs' (1964) parallelogram model for “pick any/n” data, which result from each of a number of subjects having selected a number of objects (s)he likes most from a prespecified set ofn objects. In the model, persons and objects are represented in a low dimensional space defined by a set of ordinal variables with a prespecified number of categories; objects are represented as points and persons as intervals on each dimension. A conjunctive combination rule is assumed implying that a person selects an object if and only if the object is within the subject's interval on each dimension. An algorithm for fitting the model to a data set is presented and evaluated in a simulation study. The model is illustrated with data on preferences regarding holiday trips.