We propose a generic model for the "weighted voting" aggregation step performed by several methods in supervised classification. Further, we construct an algorithm to count the number of distinct aggregate classifiers that arise in this model. When there are only two classes in the classification problem, we show that a class of functions that arises from aggregate classifiers coincides with the class of self-dual positive threshold Boolean functions
In this paper, we study the accuracy of values aggregated over classes predicted by a classification...
Part 2: Learning-Ensemble LearningInternational audienceAn ensemble of distributed neural network cl...
This paper deals with supervised learning for classification. A new general purpose classifier is pr...
AbstractWe propose a generic model for the “weighted voting” aggregation step performed by several m...
Abstract: There are many methods to design classifiers for the supervised classification problem. In...
There are many methods to design classifiers for the supervised classification problem. In this pape...
Traditionally, bagging takes a majority vote among a number of classifiers. An alternative is to agg...
International audienceThe aim of judgment aggregation is to make collective decisions based on the j...
Vote-boosting is a sequential ensemble learning method in which the individual classi ers are built...
When a multiple classifier system is employed, one of the most popular methods to accomplish the cla...
AbstractEnsembles of artificial neural networks show improved generalization capabilities that outpe...
In many ensemble classification paradigms, the function which combines local/base classifier decisio...
The idea of voting multiple decision rules was introduced in to statistics by Breiman. He used boots...
Many decision-making situations require the evaluation of several voters or agents. In a situation w...
In recent years the introduction of aggregation methods led to many new techniques within the field ...
In this paper, we study the accuracy of values aggregated over classes predicted by a classification...
Part 2: Learning-Ensemble LearningInternational audienceAn ensemble of distributed neural network cl...
This paper deals with supervised learning for classification. A new general purpose classifier is pr...
AbstractWe propose a generic model for the “weighted voting” aggregation step performed by several m...
Abstract: There are many methods to design classifiers for the supervised classification problem. In...
There are many methods to design classifiers for the supervised classification problem. In this pape...
Traditionally, bagging takes a majority vote among a number of classifiers. An alternative is to agg...
International audienceThe aim of judgment aggregation is to make collective decisions based on the j...
Vote-boosting is a sequential ensemble learning method in which the individual classi ers are built...
When a multiple classifier system is employed, one of the most popular methods to accomplish the cla...
AbstractEnsembles of artificial neural networks show improved generalization capabilities that outpe...
In many ensemble classification paradigms, the function which combines local/base classifier decisio...
The idea of voting multiple decision rules was introduced in to statistics by Breiman. He used boots...
Many decision-making situations require the evaluation of several voters or agents. In a situation w...
In recent years the introduction of aggregation methods led to many new techniques within the field ...
In this paper, we study the accuracy of values aggregated over classes predicted by a classification...
Part 2: Learning-Ensemble LearningInternational audienceAn ensemble of distributed neural network cl...
This paper deals with supervised learning for classification. A new general purpose classifier is pr...