Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new model for supervised classification based on probabilistic decision graphs is introduced. A probabilistic decision graph (PDG) is a graphical model that efficiently captures certain context specific independencies that are not easily represented by other graphical models traditionally used for classification, such as the Naive Bayes (NB) or Classification Trees (CT). This means that the PDG model can capture some distributions using fer parameters than classical models. Two approaches for constructing a PDG for classification are proposed. The first is to directly construct the model from a dataset of labelled data, while the second is to transform a previously obtained Bayesian classifier into a PDG model that can then be refined. These two approaches are compared with a wide range of classical approaches to the supervised classification problem on a number of both real world databases and artificially generated data.
['Jens Dalgaard Nielsen', 'Rafael Rumí', 'Antonio Salmerón']
I am writing a research paper on a idea Supervised classification using probabilistic decision graphs
[{"from": "human", "value": "I am writing a research paper on a idea Supervised classification using probabilistic decision graphs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new model for supervised classification based on probabilistic decision graphs is introduced. A probabilistic decision graph (PDG) is a graphical model that efficiently captures certain context specific independencies that are not easily represented by other graphical models traditionally used for classification, such as the Naive Bayes (NB) or Classification Trees (CT). This means that the PDG model can capture some distributions using fer parameters than classical models. Two approaches for constructing a PDG for classification are proposed. The first is to directly construct the model from a dataset of labelled data, while the second is to transform a previously obtained Bayesian classifier into a PDG model that can then be refined. These two approaches are compared with a wide range of classical approaches to the supervised classification problem on a number of both real world databases and artificially generated data."}]