Add to ORKGAbstract. In this paper, we consider supervised learning under the as-sumption that the available memory is small compared to the dataset size. This general framework is relevant in the context of big data, dis-tributed databases and embedded systems. We investigate a very simple, yet effective, ensemble framework that builds each individual model of the ensemble from a random patch of data obtained by drawing ran-dom subsets of both instances and features from the whole dataset. We carry out an extensive and systematic evaluation of this method on 29 datasets, using decision tree-based estimators. With respect to popular ensemble methods, these experiments show that the proposed method provides on par performance in terms of accuracy while...
This paper develops formal statistical inference procedures for machine learning ensemble methods. E...
a.kolcz @ ieee.org Handling massive datasets is a difficult problem not only due to prohibitively la...
In recent decades, the development of ensemble learning methodologies has gained a significant atten...
peer reviewedIn this paper, we consider supervised learning under the assumption that the available ...
Many simulation data sets are so massive that they must be distributed among disk farms attached to ...
This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds ...
Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a ...
In this paper we make an extensive study of different methods for building ensembles of classifiers....
We introduce BoostEM, a semi-supervised ensemble method which combines the benefits of using an ense...
This dissertation is about classification methods and class probability prediction. It can be roughl...
Abstract Ensemble learning learns from the training data by generating an ensemble of multiple base ...
As data grows in size and complexity, scientists are relying more heavily on learning algorithms tha...
In the current big data era, naive implementations of well-known learning algorithms cannot efficien...
Big data, generated from various business internet and social media activities, has become a big ch...
Abstract In machine learning, ensemble methods combine the predictions of multiple base learners to ...
This paper develops formal statistical inference procedures for machine learning ensemble methods. E...
a.kolcz @ ieee.org Handling massive datasets is a difficult problem not only due to prohibitively la...
In recent decades, the development of ensemble learning methodologies has gained a significant atten...
peer reviewedIn this paper, we consider supervised learning under the assumption that the available ...
Many simulation data sets are so massive that they must be distributed among disk farms attached to ...
This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds ...
Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a ...
In this paper we make an extensive study of different methods for building ensembles of classifiers....
We introduce BoostEM, a semi-supervised ensemble method which combines the benefits of using an ense...
This dissertation is about classification methods and class probability prediction. It can be roughl...
Abstract Ensemble learning learns from the training data by generating an ensemble of multiple base ...
As data grows in size and complexity, scientists are relying more heavily on learning algorithms tha...
In the current big data era, naive implementations of well-known learning algorithms cannot efficien...
Big data, generated from various business internet and social media activities, has become a big ch...
Abstract In machine learning, ensemble methods combine the predictions of multiple base learners to ...
This paper develops formal statistical inference procedures for machine learning ensemble methods. E...
a.kolcz @ ieee.org Handling massive datasets is a difficult problem not only due to prohibitively la...
In recent decades, the development of ensemble learning methodologies has gained a significant atten...