Catálogo de publicaciones - libros

In possibility theory, is commonly used to measure the level of conflict in information from multiple sources after merging, especially conjunctive merging. However, as shown in [HL05,Liu06b], this measure alone is not enough when pairs of uncertain information have the same degree of inconsistency, since it is not possible to tell which pair contains information that is actually , in the sense that the two pieces of information in one pair agree with each other more than the information does in other pairs. In this paper, we investigate what additional measures can be used to judge the between two pieces of uncertain information. We deploy the concept of developed in DS theory in [Liu06a], since possibility theory can be viewed as a special case of DS theory. We present properties that reveal the interconnections and differences between the degree of inconsistency and the distance between betting commitments. We also discuss how to use these two measures together to guide the possible selection of various merging operators in possibility theory.

Possibilistic logic provides a convenient tool for dealing with inconsistency and handling uncertainty. In this paper, we propose possibilistic description logics as an extension of description logics. We give semantics and syntax of possibilistic description logics. We then define two inference services in possibilistic description logics. Since possibilistic inference suffers from the , we consider a drowning-free variant of possibilistic inference, called linear order inference. Finally, we implement the algorithms for inference services in possibilistic description logics using KAON2 reasoner.

This paper addresses the issue of measuring similarity between pieces of uncertain information in the framework of possibility theory. In a first part, natural properties of such functions are proposed and a survey of the few existing measures is presented. Then, a new measure so-called Information Affinity is proposed to overcome the limits of the existing ones. The proposed function is based on two measures, namely, a classical informative distance, e.g. Manhattan distance which evaluates the difference, degree by degree, between two normalized possibility distributions and the well known inconsistency measure which assesses the conflict between the two possibility distributions. Some potential applications of the proposed measure are also mentioned in this paper.

We face the problem of constructing a model which is suited for an effective evaluation of the quality of a health-care provider: to this purpose, we focus on some relevant indicators characterizing the various services run by the provider. We rely on a fuzzy modeling approach by using the interpretation (in terms of coherent conditional probability) of a membership function of a fuzzy set as a suitable likelihood.

We propose a method which, given a document to be classified, automatically generates an ordered set of appropriate descriptors extracted from a thesaurus. The method creates a Bayesian network to model the thesaurus and uses probabilistic inference to select the set of descriptors having high posterior probability of being relevant given the available evidence (the document to be classified). We apply the method to the classification of parliamentary initiatives in the regional Parliament of Andalucía at Spain from the Eurovoc thesaurus.

This paper describes an approach for the use of genetic programming (GP) in classification problems and it is evaluated on the automatic classification problem of pollen cell images. In this work, a new reproduction scheme and a new fitness evaluation scheme are proposed as advanced techniques for GP classification applications. Also an effective set of pollen cell image features is defined for cell images. Experiments were performed on Bangor/Aberystwyth Pollen Image Database and the algorithm is evaluated on challenging test configurations. We reached at 96 % success rate on the average together with significant improvement in the speed of convergence.

This article discusses a potential application of radio frequency identification (RFID) and collaborative filtering for targeted advertising in grocery stores. Every day hundreds of items in grocery stores are marked down for promotional purposes. Whether these promotions are effective or not depends primarily on whether the customers are aware of them or not, and secondarily whether the customers are interested in the products or not. Currently, the companies are incapable of influencing the customers’ decisionmaking process while they are shopping. However, the capabilities of RFID technology enable us to transfer the recommendation systems of e-commerce to grocery stores. In our model, using RFID technology, we get real time information about the products placed in the cart during the shopping process. Based on that information we inform the customer about those promotions in which the customer is likely to be interested in. The selection of the product advertised is a dynamic decision making process since it is based on the information of the products placed inside the cart while customer is shopping. Collaborative filtering will be used for the identification of the advertised product and Bayesian networks will be used for the application of collaborative filtering. We are assuming a scenario where all products have RFID tags, and grocery carts are equipped with RFID readers and screens that would display the relevant promotions.

In this paper is presented a modeling of assessment systems of taxonomies using fuzzy logic. Specifically the taxonomies system solo is studied, which can be applied in a wide range of fields of diagnostic science. In what concerns education, the test correction is extremely hard and demands experts that are not always available. The intelligent system offers the opportunity to evaluate and classify students’ performance according to the structure of the observed learning outcome, concerning the cognitive development of the students in the field of mathematics. The system was tested on high school and university students.