Disturbing Neighbors ( ) is a method for improving the diversity of the base classifiers of any ensemble algorithm.
Abstract. Ensembles need their base classifiers do not always agree for any prediction (diverse base classifiers). Disturbing Neighbors (DN) is a method.
Disturbing Neighbors (DN\mathcal{DN}) is a method for improving the diversity of the base classifiers of any ensemble algorithm. DN\mathcal{DN} builds for each ...
This paper presents an experimental validation on 62 UCI datasets for standard ensemble methods using Support Vector Machines (SVM) with a linear kernel as ...
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Disturbing Neighbors (DN) is a method for generating classifier ensembles. Moreover, it can be combined with any other ensemble method, generally improving ...
Missing: SVM. | Show results with:SVM.
This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) ...
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Disturbing neighbors ensembles of trees for imbalanced data. Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012.
This work explores two approaches to event-driven predictive maintenance in Industry 4.0 that cast the problem at hand as a classification or a regression ...
Aug 22, 2023 · Four popular classification algorithms are: Decision Tree; Random Forest; Support Vector Machine (SVM); K-Nearest Neighbour. Decision Tree. A ...
This paper presents an experimental study over 62 UCI datasets of three types of RPs taking into account the size of the projected space and using linear SVMs ...