Add to ORKGThe aim of this paper is to propose a simple procedure that a priori determines a minimum number of classifiers to combine in order to obtain a prediction accuracy level similar to the one obtained with the combination of larger ensembles. The procedure is based on the McNe- mar non-parametric test of significance. Knowing a priori the minimum size of the classifier ensemble giving the best prediction accuracy, constitutes a gain for time and memory costs especially for huge data bases and real-time applications. Here we applied this procedure to four multiple classifier systems with C4.5 decision tree (Breiman's Bagging, Ho's Random subspaces, their combination we labeled ‘Bagfs', and Breiman's Random forests) and five large benchmark data...
Decision trees are most often made using the heuristic that a series of locally optimal decisions yi...
Random forests are ensembles of randomized decision trees where diversity is created by injecting ra...
Decision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, ...
Machine learning methods can be used for estimating the class membership probability of an observati...
We experimentally evaluate bagging and seven other randomization-based approaches to creating an ens...
International audienceIn this paper we present a study on the random forest (RF) family of ensemble ...
Random forests have been introduced by Leo Breiman (2001) as a new learning algorithm, extend-ing th...
Abstract. Ensemble methods are able to improve the predictive performance of many base classifiers. ...
We introduce a broad family of decision trees, Composite Trees, whose leaf classifiers are selected ...
Several studies have shown that combining machine learning models in an appropriate way will introdu...
The Random Forests ensemble predictor has proven to be well-suited for solving a multitudeof differe...
Random forest is an ensemble method that combines many decision trees. Each level of trees is determ...
Classification is a process where a classifier predicts a class label to an object using the set of ...
The Probabilistic random forest is a classification model which chooses a subset of features for eac...
The predictive performance of a random forest ensemble is highly associated with the strength of ind...
Decision trees are most often made using the heuristic that a series of locally optimal decisions yi...
Random forests are ensembles of randomized decision trees where diversity is created by injecting ra...
Decision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, ...
Machine learning methods can be used for estimating the class membership probability of an observati...
We experimentally evaluate bagging and seven other randomization-based approaches to creating an ens...
International audienceIn this paper we present a study on the random forest (RF) family of ensemble ...
Random forests have been introduced by Leo Breiman (2001) as a new learning algorithm, extend-ing th...
Abstract. Ensemble methods are able to improve the predictive performance of many base classifiers. ...
We introduce a broad family of decision trees, Composite Trees, whose leaf classifiers are selected ...
Several studies have shown that combining machine learning models in an appropriate way will introdu...
The Random Forests ensemble predictor has proven to be well-suited for solving a multitudeof differe...
Random forest is an ensemble method that combines many decision trees. Each level of trees is determ...
Classification is a process where a classifier predicts a class label to an object using the set of ...
The Probabilistic random forest is a classification model which chooses a subset of features for eac...
The predictive performance of a random forest ensemble is highly associated with the strength of ind...
Decision trees are most often made using the heuristic that a series of locally optimal decisions yi...
Random forests are ensembles of randomized decision trees where diversity is created by injecting ra...
Decision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, ...