Dynamic (also known as instance-based) ensemble pruning, selects a (potentially) different subset of models from an ensemble during prediction based on the given unknown instance with the goal of maximizing prediction accuracy. This paper models dynamic ensemble pruning as a multi-label classification task, by considering the members of the ensemble as labels. Multi-label training examples are ...

In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases:...

We present an approach for the automatic classification of Nuclear Magnetic Resonance Spectroscopy data of biofluids with respect to drug induced organ toxicities. Classification is realized by an Ensemble of Support Vector Machines, trained on different subspaces according to a modified version of Random Subspace Sampling. Features most likely leading to an improved classification accuracy are...

Ensemble classification methods have been shown to produce more accurate predictions than the base component models (Bauer and Kohavi 1999). Due to their effectiveness, ensemble approaches have been applied in a wide range of domains to improve classification. The expected prediction error of classification models can be decomposed into bias and variance (Friedman 1997). Ensemble methods that i...

Multilabel learning goes beyond standard supervised models by associating a sample with more than one class label. Among the many techniques developed in last decade to handle multilabel best approaches are those harnessing power of ensembles and deep learners. This work proposes merging both methods combining set gated recurrent units, temporal convolutional neural networks, long short-term me...