Add to ORKGKeywords:Rough set; mutual information; Bayesian network; structure learning Abstract. In Bayesian network structure learning for incomplete data set, a common problem is too many attributes causing low efficiency and high computation complexity. In this paper, an algorithm of attribute reduction based on rough set is introduced. The algorithm can effectively reduce the dimension of attributes and quickly determine the network structure using mutual information for Bayesian network structure learning
Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways...
There are two categories of well-known approach (as basic principle of classification process) for l...
Abstract:- Rough sets theory is an effective mathematical tool dealing with vagueness and uncertaint...
Abstract—In this paper, an algorithm with attribute reduction based on rough set is introduced. The ...
This paper presents a method of Bayesian network construction from data. Many technical problems lik...
[[abstract]]For data mining or machine learning, the plethora of parameters that may affect the effi...
As the combination of parameter learning and structure learning, learning Bayesian networks can also...
Abstract. In recent years there has been a growing interest in Bayesian Network learning from uncert...
Abstract: There are different structure of the network and the variables, and the process of learnin...
The attribute reduction problem for rough set is analyzed by the mutual information of attribute set...
It is a challenging task of learning a large Bayesian network from a small data set. Most convention...
The learning of a Bayesian network structure, especially in the case of wide domains, can be a compl...
The learning of a Bayesian network structure, especially in the case of wide domains, can be a compl...
The learning of a Bayesian network structure, especially in the case of wide domains, can be a compl...
The learning of a Bayesian network structure, especially in the case of wide domains, can be a compl...
Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways...
There are two categories of well-known approach (as basic principle of classification process) for l...
Abstract:- Rough sets theory is an effective mathematical tool dealing with vagueness and uncertaint...
Abstract—In this paper, an algorithm with attribute reduction based on rough set is introduced. The ...
This paper presents a method of Bayesian network construction from data. Many technical problems lik...
[[abstract]]For data mining or machine learning, the plethora of parameters that may affect the effi...
As the combination of parameter learning and structure learning, learning Bayesian networks can also...
Abstract. In recent years there has been a growing interest in Bayesian Network learning from uncert...
Abstract: There are different structure of the network and the variables, and the process of learnin...
The attribute reduction problem for rough set is analyzed by the mutual information of attribute set...
It is a challenging task of learning a large Bayesian network from a small data set. Most convention...
The learning of a Bayesian network structure, especially in the case of wide domains, can be a compl...
The learning of a Bayesian network structure, especially in the case of wide domains, can be a compl...
The learning of a Bayesian network structure, especially in the case of wide domains, can be a compl...
The learning of a Bayesian network structure, especially in the case of wide domains, can be a compl...
Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways...
There are two categories of well-known approach (as basic principle of classification process) for l...
Abstract:- Rough sets theory is an effective mathematical tool dealing with vagueness and uncertaint...