Add to ORKGEnsemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its general-ization error. We present a new method for generating ensembles, DECORATE (Diverse En-semble Creation by Oppositional Relabeling of Artificial Training Examples), that directly constructs diverse hypotheses using additional artificially-constructed training examples. The technique is a simple, general meta-learner that can use any strong learner as a base classifier to build diverse committees. Experimental results using decision-tree induction as a base learner demonstrate that this ...
In machine learning, ensemble methods combine the predictions of multiple base learners to construct...
When performing predictive data mining, the use of ensembles is known to increase prediction accurac...
In this paper, we propose a method to generate an optimized ensemble classifier. In the proposed met...
The diversity of an ensemble of classifiers is known to be an important factor in determining its ge...
textEnsemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses a...
Popular ensemble classifier induction algorithms, such as bagging and boosting, construct the ensemb...
Abstract—Ensemble learning strategies, especially Boosting and Bagging decision trees, have demonstr...
The goal of an ensemble construction with several classifiers is to achieve better generalization t...
The goal of an ensemble construction with several classifiers is to achieve better generalization t...
Ensemble learning strategies, especially Boosting and Bagging decision trees, have demonstrated impr...
One of the general techniques for improving classification accuracy is learning ensembles of classif...
In this paper, we propose an incremental ensemble classifier learning method. In the proposed method...
In this paper, we propose an incremental ensemble classifier learning method. In the proposed method...
In machine learning, ensemble methods combine the predictions of multiple base learners to construct...
In real world situations every model has some weaknesses and will make errors on training data. Give...
In machine learning, ensemble methods combine the predictions of multiple base learners to construct...
When performing predictive data mining, the use of ensembles is known to increase prediction accurac...
In this paper, we propose a method to generate an optimized ensemble classifier. In the proposed met...
The diversity of an ensemble of classifiers is known to be an important factor in determining its ge...
textEnsemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses a...
Popular ensemble classifier induction algorithms, such as bagging and boosting, construct the ensemb...
Abstract—Ensemble learning strategies, especially Boosting and Bagging decision trees, have demonstr...
The goal of an ensemble construction with several classifiers is to achieve better generalization t...
The goal of an ensemble construction with several classifiers is to achieve better generalization t...
Ensemble learning strategies, especially Boosting and Bagging decision trees, have demonstrated impr...
One of the general techniques for improving classification accuracy is learning ensembles of classif...
In this paper, we propose an incremental ensemble classifier learning method. In the proposed method...
In this paper, we propose an incremental ensemble classifier learning method. In the proposed method...
In machine learning, ensemble methods combine the predictions of multiple base learners to construct...
In real world situations every model has some weaknesses and will make errors on training data. Give...
In machine learning, ensemble methods combine the predictions of multiple base learners to construct...
When performing predictive data mining, the use of ensembles is known to increase prediction accurac...
In this paper, we propose a method to generate an optimized ensemble classifier. In the proposed met...
