Add to ORKGIn this paper we present a novel approach for combining GP-based ensembles by means of a Bayesian Network. The proposed system is able to effectively learn decision tree en-sembles using two different strategies: decision trees ensem-bles are learned by means of boosted GP algorithm; the responses of the learned ensembles are combined using a Bayesian network, which also implements a selection strat-egy that reduces the size of the built ensembles
To address the classification problem when the number of cases is too small to effectively use just ...
We describe a classifier made of an ensemble of decision trees, designed using information theory co...
Classifier ensembles have been one of the main topics of interest in the neural networks, machine le...
Ensemble techniques have been widely used to improve classication performance also in the case of GP...
Combining classifier methods have shown their effective-ness in a number of applications. Nonetheles...
We propose an algorithm for compiling Bayesian network classifiers into decision graphs that mimic t...
When predictive modeling requires comprehensible models, most dataminers will use specialized techni...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
Classification is the process of constructing (learning) a model (classifier) to predict the class (...
Classification of real-world data poses a number of chal-lenging problems. Mismatch between classifi...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a ...
The use of Bayesian networks for classification problems has received significant recent attention. ...
One of the general techniques for improving classification accuracy is learning ensembles of classif...
This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing th...
To address the classification problem when the number of cases is too small to effectively use just ...
We describe a classifier made of an ensemble of decision trees, designed using information theory co...
Classifier ensembles have been one of the main topics of interest in the neural networks, machine le...
Ensemble techniques have been widely used to improve classication performance also in the case of GP...
Combining classifier methods have shown their effective-ness in a number of applications. Nonetheles...
We propose an algorithm for compiling Bayesian network classifiers into decision graphs that mimic t...
When predictive modeling requires comprehensible models, most dataminers will use specialized techni...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
Classification is the process of constructing (learning) a model (classifier) to predict the class (...
Classification of real-world data poses a number of chal-lenging problems. Mismatch between classifi...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a ...
The use of Bayesian networks for classification problems has received significant recent attention. ...
One of the general techniques for improving classification accuracy is learning ensembles of classif...
This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing th...
To address the classification problem when the number of cases is too small to effectively use just ...
We describe a classifier made of an ensemble of decision trees, designed using information theory co...
Classifier ensembles have been one of the main topics of interest in the neural networks, machine le...