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Bayes networks are powerful tools for decision and reasoning under uncertainty. A very simple form of Bayes networks is called naive Bayes, which are particularly efficient for inference tasks. However, naive Bayes are based on a very strong independence assumption. This paper offers an experimental study of the use of naive Bayes in intrusion detection. We(More)
This paper presents a method for assessing the reliability of a sensor in a classification problem based on the transferable belief model. First, we develop a method for the evaluation of the reliability of a sensor when considered alone. The method is based on finding the discounting factor minimizing the distance between the pignistic probabilities(More)
This paper extends the decision tree technique to an uncertain environment where the uncertainty is represented by belief functions as interpreted in the Transferable Belief Model (TBM). This so-called belief decision tree is a new classification method adapted to uncertain data. We will be concerned with the construction of the belief decision tree from a(More)
Clustering is one of the most useful methods of intelligent engineering domain, in which a set of similar objects are categorized into clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough(More)
Bayesian networks are powerful tools for decision and reasoning under uncertainty. A very simple form of these networks is called naive Bayes, which is particularly efficient for learning and inference tasks. This paper offers an experimental study of the use of naive Bayes in intrusion detection. We show that eventhough they have a simple structure, naive(More)
This paper surveys the major works related to an artificial immune system based classifier that was proposed in the 2000s, namely, the artificial immune recognition system (AIRS) algorithm. This survey has revealed that most works on AIRS was dedicated to the application of the algorithm to real-world problems rather than to theoretical developments of the(More)
This paper addresses the classification problem with imperfect data. More precisely, it extends standard decision trees to handle uncertainty in both building and classification procedures. Uncertainty here is represented by means of possibility distributions. The first part investigates the issue of building decision trees from data with uncertain class(More)
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(More)