We present a variation of a method of classification based in uncertainty on credal set. Similarly to its origin it use the imprecise Dirichlet model to create the credal set and the same uncertainty measures. It take into account sets of two variables to reduce the uncertainty and to seek the direct relations between the variables in the data base and the variable to be classified. The success are equivalent to the success of the first method except in those where there are a direct relations between some variables that decide the value of the variable to be classified where we have a notable improvement
The goal of high-level information fusion is to provide effective decision-support regarding situati...
This paper proposes a definition of relative uncertainty aversion for decision models under complete...
AbstractThis paper addresses the classification problem with imperfect data. More precisely, it exte...
International audienceIn this paper we present a new credal classification rule (CCR) based on belie...
This paper reports on an investigation in classification technique employed to classify noised and u...
A general way of representing incomplete information is to use closed and convex sets of probability...
Modeling and managing uncertainty in the classification problem remains an important and interesting...
Classifiers that aim at doing credible predictions should rely on carefully elicited prior knowledge...
Classification of high dimensional data finds wide-ranging applications. In many of these applicatio...
AbstractCredal networks are models that extend Bayesian nets to deal with imprecision in probability...
AbstractThe aim of this paper is to formalize, within a broad range of theories of imprecise probabi...
In this chapter, we consider the problem of the elicitation and specification of an uncertainty dist...
summary:When proposing and processing uncertainty decision-making algorithms of various kinds and pu...
Ben-Israel and Iyigun ([1] and [2]) presents a new clustering method which is probabilistic distance...
Abstract. Credal Decision Trees (CDTs) are algorithms to design clas-sifiers based on imprecise prob...
The goal of high-level information fusion is to provide effective decision-support regarding situati...
This paper proposes a definition of relative uncertainty aversion for decision models under complete...
AbstractThis paper addresses the classification problem with imperfect data. More precisely, it exte...
International audienceIn this paper we present a new credal classification rule (CCR) based on belie...
This paper reports on an investigation in classification technique employed to classify noised and u...
A general way of representing incomplete information is to use closed and convex sets of probability...
Modeling and managing uncertainty in the classification problem remains an important and interesting...
Classifiers that aim at doing credible predictions should rely on carefully elicited prior knowledge...
Classification of high dimensional data finds wide-ranging applications. In many of these applicatio...
AbstractCredal networks are models that extend Bayesian nets to deal with imprecision in probability...
AbstractThe aim of this paper is to formalize, within a broad range of theories of imprecise probabi...
In this chapter, we consider the problem of the elicitation and specification of an uncertainty dist...
summary:When proposing and processing uncertainty decision-making algorithms of various kinds and pu...
Ben-Israel and Iyigun ([1] and [2]) presents a new clustering method which is probabilistic distance...
Abstract. Credal Decision Trees (CDTs) are algorithms to design clas-sifiers based on imprecise prob...
The goal of high-level information fusion is to provide effective decision-support regarding situati...
This paper proposes a definition of relative uncertainty aversion for decision models under complete...
AbstractThis paper addresses the classification problem with imperfect data. More precisely, it exte...