Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules

When combining classifiers in the Dempster-Shafer framework, Dempster’s rule is generally used. However, this rule assumes the classifiers to be independent. This paper investigates the use of other operators for combining non independent classifiers, including the cautious rule and, more generally, t-norm based rules with behavior ranging between Dempster’s rule and the cautious rule. Two strategies are investigated for learning an optimal combination scheme, based on a parameterized family of t-norms. The first one learns a single rule by minimizing an error criterion. The second strategy is a two-step procedure, in which groups of classifiers with similar outputs are first identified using a clustering algorithm. Then, within- and between-cluster rules are determined by minimizing an error criterion. Experiments with various synthetic and real data sets demonstrate the effectiveness of both the single rule and two-step strategies. Overall, optimizing a single t-norm based rule yields better results than using a fixed rule, including Dempster’s rule, and the two-step strategy brings further improvements.     

从批评话语分析视角解读妈祖封号

以系统功能语言学理论为指导,以妈祖封号为例,运用批评话语分析的方法和原则,分析了分类、及物性、情态等方面的语言特点,剖析导致这些语言选择的社会因素,发现其语言选择反映了历代封建王朝的意识形态,即利用妈祖神力为其权力服务,以统治的形式推行自己的意志、干预民间。分析也说明了系统功能语言学在汉语特殊语类中的可操作性和适用性。     

基于改进的混沌鲸鱼算法优化DBN的广东人口数量预测研究

为了估算广东省人口总数和老龄化人口总数,针对深度置信网络模型的性能易受其权值和阈值选择的影响,提出一种基于改进的混沌鲸鱼算法优化DBN的人口数量预测模型.首先,为提高鲸鱼算法的收敛速度和避免局部最优,将Skew Tent混沌模型和非线性收敛因子引入WOA算法用于初始化WOA种群和改进WOA更新策略.其次为了提高DBN模...     

明清时期海神妈祖神格外化形象分析

从明清时期海神妈祖神格外化角度切入,用美学的方法分析其外化形象——“神火”“神鸟”这两种形象背后所蕴合的象征义。论述两者既象征了光明、希望、吉祥,又象征了含有融合性的适变性。理解两者的象征义,有助于深化理解海神妈祖的精神内核。     

妈祖信仰与文化产业：人类学的个案研究——以台湾嘉义新港奉天宫为例

以田野调查资料为依据,阐述台湾嘉义县新港乡奉天宫妈祖庙,在现任董监事团体的积极经营之下,在南台湾众多老大妈祖庙竞争中,崭露头角。奉天宫的成功转型经历,为妈祖信仰与文化产业创新,提供了典型的个案。     

Distances in evidence theory: Comprehensive survey and generalizations

The purpose of the present work is to survey the dissimilarity measures defined so far in the mathematical framework of evidence theory, and to propose a classification of these measures based on their formal properties. This research is motivated by the fact that while dissimilarity measures have been widely studied and surveyed in the fields of probability theory and fuzzy set theory, no comprehensive survey is yet available for evidence theory. The main results presented herein include a synthesis of the properties of the measures defined so far in the scientific literature; the generalizations proposed naturally lead to additions to the body of the previously known measures, leading to the definition of numerous new measures. Building on this analysis, we have highlighted the fact that Dempster’s conflict cannot be considered as a genuine dissimilarity measure between two belief functions and have proposed an alternative based on a cosine function. Other original results include the justification of the use of two-dimensional indexes as (cosine; distance) couples and a general formulation for this class of new indexes. We base our exposition on a geometrical interpretation of evidence theory and show that most of the dissimilarity measures so far published are based on inner products, in some cases degenerated. Experimental results based on Monte Carlo simulations illustrate interesting relationships between existing measures.     

An algebraic theory for statistical information based on the theory of hints

Statistical problems were at the origin of the mathematical theory of evidence, or Dempster–Shafer theory. It was also one of the major concerns of Philippe Smets, starting with his PhD dissertation. This subject is reconsidered here, starting with functional models, describing how data is generated in statistical experiments. Inference is based on these models, using probabilistic assumption-based reasoning. It results in posterior belief functions on the unknown parameters. Formally, the information used in the process of inference can be represented by hints. Basic operations on hints are combination, corresponding to Dempster’s rule, and focussing. This leads to an algebra of hints. Applied to functional models, this introduces an algebraic flavor into statistical inference. It emphasizes the view that in statistical inference different pieces of information have to be combined and then focussed onto the question of interest. This theory covers Bayesian and Fisher type inference as two extreme cases of a more general theory of inference.     

Clustering decomposed belief functions using generalized weights of conflict

We develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions into simple support and inverse simple support functions that are clustered based on their pairwise generalized weights of conflict, constrained by weights of attraction assigned to keep track of all decompositions. The generalized conflict c(-∞,∞) and generalized weight of conflict J^-(-∞,∞) are derived in the combination of simple support and inverse simple support functions.     

Integral representation of belief measures on compact spaces

An integral representation theorem for outer continuous and inner regular belief measures on compact topological spaces is elaborated under the condition that compact sets are countable intersections of open sets (e.g. metric compact spaces). Extreme points of this set of belief measures are identified with unanimity games with compact support. Then, the Choquet integral of a real valued continuous function can be expressed as a minimum of means over the sigma-core and also as a mean of minima over the compact subsets. Similarly, for bounded measurable functions, the Choquet integral is expressed as min of means over the core, we prove in addition that it is a mean of infima over the compact subsets. Then, we obtain Choquet–Revuz' measure representation theorem and introduce the Möbius transform of a belief measure. An extension to locally compact and sigma-compact topological spaces is provided.     

Uncertainty about fundamentals and herding behavior in the FOREX market

It is traditionally assumed in finance models that the fundamental value of assets is known with certainty. Although this is an appealing simplifying assumption it is by no means based on empirical evidence. A simple heterogeneous agent model of the exchange rate is presented. In the model, traders do not observe the true underlying fundamental exchange rate and as a consequence they base their trades on beliefs about this variable. Despite the fact that only fundamentalist traders operate in the market, the model belongs to the heterogeneous agent literature, as traders have different beliefs about the fundamental rate.